{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5922b2dc-e932-436c-8eed-5ac2a73d4d35",
   "metadata": {},
   "source": [
    "## 线性回归 linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a6ac17fe-38da-4db4-aa48-b207a10d40d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import cross_val_predict, train_test_split\n",
    "from sklearn import datasets\n",
    "from sklearn.datasets import fetch_california_housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "05104fa3-fe41-4345-897e-e0ab97ba633a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
    "raw_df = pd.read_csv(data_url, sep=\"\\\\s+\", skiprows=22, header=None)\n",
    "data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
    "target = raw_df.values[1::2, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3bad9263-aa67-46cb-8d97-92d8c86f3bd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(455, 13)\n",
      "(51, 13)\n"
     ]
    }
   ],
   "source": [
    "# 数据集划分\n",
    "X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.1, random_state=91)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "58bfb4ce-2ca3-439d-8ecf-9b31e45b3f4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.41700e-02, 0.00000e+00, 5.96000e+00, ..., 1.92000e+01,\n",
       "        3.96900e+02, 9.68000e+00],\n",
       "       [1.44550e-01, 1.25000e+01, 7.87000e+00, ..., 1.52000e+01,\n",
       "        3.96900e+02, 1.91500e+01],\n",
       "       [8.25260e-01, 2.00000e+01, 3.97000e+00, ..., 1.30000e+01,\n",
       "        3.93420e+02, 1.12500e+01],\n",
       "       ...,\n",
       "       [3.67822e+00, 0.00000e+00, 1.81000e+01, ..., 2.02000e+01,\n",
       "        3.80790e+02, 1.01900e+01],\n",
       "       [8.44700e-02, 0.00000e+00, 4.05000e+00, ..., 1.66000e+01,\n",
       "        3.93230e+02, 9.64000e+00],\n",
       "       [6.64200e-02, 0.00000e+00, 4.05000e+00, ..., 1.66000e+01,\n",
       "        3.91270e+02, 6.92000e+00]], shape=(455, 13))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7ed62788-10bc-4688-bdf0-51390351a8d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.30765695e-01  5.24208286e-02  3.37932648e-02  1.87765392e+00\n",
      " -1.95164049e+01  3.56605769e+00  3.89425300e-03 -1.56576809e+00\n",
      "  3.42133559e-01 -1.40562860e-02 -9.85963260e-01  9.76653966e-03\n",
      " -5.19814806e-01]\n",
      "39.692907206707886\n"
     ]
    }
   ],
   "source": [
    "# 模型训练\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "print(lr.coef_)\n",
    "print(lr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3c0763e2-8bf6-45cf-8d9a-28a589d9f41e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([27.59883614, 14.40109082, 22.53716648, 23.45359945, 16.33288919,\n",
       "        40.70524372, 23.24422355, 14.6952469 , 15.29075768, 17.76298518,\n",
       "        24.01030276, 21.21304366, 33.19857467, 39.5539706 , 14.7768323 ,\n",
       "        25.4788785 , 27.55042128, 14.4131926 , -4.7216385 , 32.76900941]),\n",
       " array([36.2, 13.1, 20. , 23. , 13.3, 50. , 23.8, 11.7, 18.9, 17.8, 20.1,\n",
       "        22.7, 31.7, 50. , 15.4, 24. , 24.5, 17.2,  7. , 33.1]))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模型评估\n",
    "y_pred = lr.predict(X_test)\n",
    "y_pred[0:20],y_test[0:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "391e3044-9897-4218-adc5-48b8bd593f14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 30.887149863849594\n",
      "RMSE: 5.557620881622783\n"
     ]
    }
   ],
   "source": [
    "# ground truth\n",
    "from sklearn import metrics\n",
    "MSE = metrics.mean_squared_error(y_test, y_pred)\n",
    "RMSE = np.sqrt(metrics.mean_squared_error(y_test, y_pred))\n",
    "print('MSE:', MSE)\n",
    "print('RMSE:', RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2d0b34fb-5b6a-48be-9aef-156588d387f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.family'] = ['sans-serif']\n",
    "mpl.rcParams['font.sans-serif'] = ['SimHei']\n",
    "mpl.rcParams['axes.unicode_minus']=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "43b4c3d6-991e-4d4a-a735-4bb7f6cf0ca6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制图\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(range(len(y_test)), y_test, 'r', label='测试数据')\n",
    "plt.plot(range(len(y_test)), y_pred, 'b', label='预测数据')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "da41953c-9f9f-4f29-bee8-26d75e2066b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi0AAAGuCAYAAABcJTaZAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAR8tJREFUeJzt3Qd4VNXWxvGVhIQQSEJCC0ikCAEREUQQpKgYFaWIFwvFBpciFkARFUUsKIhKF1GK4AVR0asgglhAL4II0qugCNcIIj0EQme+Z23vzJfEJEzCzJxzZv6/5xmTc2YyHBySebP32muHuVwulwAAANhcuNUXAAAA4A1CCwAAcARCCwAAcARCCwAAcARCCwAAcARCCwAAcARCCwAAcIQiEkTOnj0ru3btktjYWAkLC7P6cgAAgBe0ZVxGRoZUqFBBwsPDQyO0aGBJTk62+jIAAEAhpKWlScWKFUMjtOgIi/svHRcXZ/XlAAAALxw+fNgMOrjfx0MitLinhDSwEFoAAHCWc5V2UIgLAAAcgdACAAAcgdACAAAcgdACAAAcgdACAAAcgdACAAAcgdACAAAcgdACAAAcgdACAAAcgdACAAAcwZLQ0rt3b9Oq132rVq2aOb9hwwZp0KCBJCQkSP/+/c2ujwAAAJaFlhUrVsjcuXPl4MGD5rZ69Wo5ceKEtGnTRurXr2/u37Rpk0ydOpVXCQAAGGGuAA9nnD59WkqVKiU7d+6UEiVKeM7PmjVLunbtKr///rvExMTI2rVr5cEHH5TFixcXaJfI+Ph4SU9PZ8NEAAAcwtv374CPtKxfv17Onj0rdevWlWLFiknLli3lt99+MyGlUaNGJrCoOnXqmNGW/OjojP5Fs94AAIBvZWZmyrhx48z7t5UCHlo0iNSoUUOmTZsm69atkyJFikiPHj1M4KhSpYrncVrrEhERYaaP8jJ06FCTzNy35OTkAP0tAAAIfi6XS2bOnCk1a9aUhx56yPKyjYBPD+WkoywaVtzFuSNGjPDcpyHkhx9+kAsuuCDPkRa9uWnw0a9heggAgPP3888/m8CiIyyVKlUyoy2tWrUSq6aHiojFypYta/5nJCUlmdVDWWVkZEhUVFSeX1u0aFFzAwAAvqGDAe731urVq0u/fv1MDaqu6tWyjpCaHtK/9IwZMzzHS5culfDwcLn00kvN527bt283/+MSExMDfYkAAIScM2fOyPjx482IysaNGz3nX3nlFRk0aJDlgcWS0HLZZZfJwIEDZcGCBfLll1/K/fffL/fcc4/ccMMNZnhoypQp5nFDhgyR1NRUU9cCAAD8Z9GiRablyAMPPCB//vmnjBkzRuwo4NNDd911l0lw7du3N4FEjzWgaEHupEmTpGPHjmY0Rkdfvv3220BfHgAAISMtLU0ef/xxef/9982xNnd94YUXzICCHVleiJvT7t27ZeXKlWb5s/ZzKQj6tAAA4J1Ro0bJ008/bZYz60KYnj17yuDBg6V06dISaI4pxM1JC3L9UZkMAAD+n45ZaGBp2rSpmQ6qV6+e2J3tQgsAAPC9zZs3m5EMnclQ2nelcuXK0q5dOzPS4gTs8gwAQBBLT0+XRx991HSav/fee+XkyZPmfGRkpNx6662OCSyK0AIAQBA6e/asWZGbkpIiI0eONHv/aaM4J295w/QQAABBZtmyZfLwww/Ljz/+aI51+xwtvNX9/pyM0AIAQBBZtWqVp24lNjZWnn32WRNg8usw7xSEFgAAgki9evVMw1bdt0/7oOmq3GBBTQsAAA42f/58ueqqq2T//v3mWAtrP/vsM3n77beDKrAoQgsAAA70yy+/SNu2beWmm24ye/e9/PLLnvt0ZVAwYnoIAAAHOXLkiJn2GT58uFm+rNvg9OnTx+zrF+wILQAAOMT7778v/fr1k127dpnjG2+80awK0qXMoYDQAgCAQ3z77bcmsFStWtWEldatWzuqOdz5IrQAAGBTe/fulWPHjsmFF15ojl988UUTWHr37i3R0dESaijEBQDAZrR77dixY0032x49epjNDZXuwPz444+HZGBRjLQAAGAjCxcuNCMpGzduNMd//vmnHDp0SBISEiTUMdICAIAN7NixQ2677Ta57rrrTGApVaqUvPnmm7JixQoCy/8w0gIAgMWWLFkiqampcvz4cQkPD5cHHnhAnn/+eUlMTLT60myF0AIAgMWuuOIKSU5OlooVK8ro0aPl0ksvtfqSbInpIQAAAmz9+vXSvXt3OXXqlDkuWrSofPfdd7JgwQICSz4ILQAABMiBAwfMjsu6qeGkSZPkjTfe8NxXrly5kOq5UhhMDwEA4GdnzpwxIeXpp5/2bGzYvn17ueWWW6y+NEchtAAA4EeLFy82oytr1qwxx7Vq1ZIxY8aYVUIoGKaHAADwo2effdYElvj4eFNkq58TWAqH0AIAgA+dOHHC7MTspkFFi25//vln0zQuMjLS0utzMkILAAA+oK3258yZI5dccompXXGrXbu2TJgwQcqUKWPp9QUDQgsAAOdpy5YtcvPNN0vbtm1l27ZtMmvWLMnMzLT6soIOoQUAgEI6fPiw9O/f34ymzJ8/X6KiouTJJ5+UDRs2SExMjNWXF3RYPQQAQCFb7+uyZd3QULVu3VpGjBgh1atXt/rSghahBQCAQtBwcuzYMfNx1KhRZnoI/sX0EAAAXtARFe2v4la2bFnTdl+ngggsgUFoAQAgHydPnjTTPikpKdKnTx/5/PPPs210qHUsCAymhwAAyMOXX35pgspPP/3kCSksXbYOIy0AAOTw66+/Srt27eTGG280gUWDyuTJk2XZsmUmuMAajLQAAJCjSVybNm1k06ZNUqRIEbNv0KBBg6RkyZJWX1rIY6QFABDyNKicPXvWfB4WFiZDhw6V66+/XtatW2fqWQgs9kBoAQCEtNWrV0vz5s1l/PjxnnM60vLFF1/IxRdfbOm1ITtCCwAgJO3bt0/uv/9+qV+/vixevFhefvllOXXqlGe0RW+wF0ILACCknD59Wl5//XXTFO6tt94yU0MdOnSQ77//nh2YbY5CXABAyPjxxx+la9eupiGcqlOnjowdO9ZMD8H+GGkBAIQMHUnRVUGJiYnyxhtvyMqVKwksDkJoAQAELd0b6KuvvvIc161bV959913ZunWr9OrVyyxphnMQWgAAQUfrVD7++GOpVauW2RfI3dFWaf1KqVKlLL0+FA6hBQAQVDZu3Gh6rLRv31527NghSUlJsnv3bqsvCz5AaAEABIVDhw5J37595bLLLjO7LxctWlSeeeYZM8pyzTXXWH158AEm8wAAQbGMWfut6J5B6tZbb5Xhw4dLlSpVrL40+BAjLQAAx9OCWm0Upx1sdWdmrWchsPjOmbMuWbptv8xes9N81GMrhLm0WilIHD58WOLj4yU9PV3i4uKsvhwAgJ/s2rVLnnjiCbnnnntM/Ypyd7OlQZxvzd/whzw/Z5P8kX7cc658fLQ826aWtKxdPqDv34y0AAAc48SJEzJs2DBJSUmR6dOnyyOPPOLZ6FDDCoHF94Gl1/RV2QKL2p1+3JzX+wOJ0AIAcIS5c+dK7dq15cknn5SjR49K48aNZerUqRIezluZP+gUkI6w5DYd4z6n9wdyqohXGgBgaz///LO0atVKWrduLb/88otZwvyvf/3LbHJ4xRVXWH15QWv59gN/G2HJSqOK3q+PCxRWDwEAbG3dunUyb948M/Wj00EDBw6U2NhYqy8r6O3JOO7TxwXFSEvLli3N8J76z3/+Yyq/S5cuLSNGjLD60gAAFtAaFR1dcfvHP/5hgopucqj1LASWwCgbG+3Txzk+tOj+D1988YX5fO/evdK2bVvp2LGjLF261Nz3zTffWHl5AAALdmFu0qSJqVc5ePCgORcWFiaDBw82xbcInIZVEs0qobA87tfzer8+LuhDy4EDB6Rfv35So0YNc6whpUKFCqZ7YfXq1WXQoEEyefJkqy4PABBAe/bskW7dusmVV14pP/zwg1klpDswwzoR4WFmWXNeZbZ6Xu/XxwV9aNHAoh0LGzVqZI7Xrl0r1157rUnUqmHDhuf8B6v/qHVtd9YbAMA5tLfKqFGjzC+r+ouqtg67++67ZcuWLZKammr15cFmLAktOu2j+0K88sornnMaOLJ2L9TmMto8KD9Dhw41zWjct+TkZL9eNwDAd44fPy6XX365Ka7V9wBtw79kyRKzMkhH3mGPJc95CQuFJc/6j7Rnz54yfvz4bMVU2oJZN7dyi46OlszMzHyfa8CAAaZ7nvuWlpbm12sHAPiO/pzX0fYyZcrIxIkTZdmyZXLVVVdZfVmw8ZLngIcWLaZq0KCBWXOfVWJioinGdcvIyJCoqKh8n0tDjo7IZL0BAOxJG8Jp3eLWrVs953Q1kB5rPUtERISl1wf7L3kOeJ+WGTNmmHBSsmRJc6yjKTNnzjSfZ03Yq1evlgsuuCDQlwcA8DGtU9Gf84899pj8/vvv5uf7Z5995vmFFfZU1oZLngMeWr777juzhbib/iPW4cH77rvP1KR8/fXXcvXVV5t6lxtvvDHQlwcA8CFdZNG7d29ZtGiROa5UqZJ07drVBBn3wgvYe8nz7vTjua4g0lcvKcBLngMeWipWrJjtuESJEqaZnN5GjhwpN998szmnIzHupnMAAGfZv3+/aV3x5ptvmmZxxYoVM3WI+ouqfg7nLHnuNX2VCShZg4s7bgZ6yXOYS+OujWzfvl1++uknadasmQkv/tjaGgDgX6+++qo8/vjj5vM77rjDHF944YVWXxYKQXdy1lVCWYtydQRGA0vL2uXFF7x9/7ZdaDkfhBYAsM6RI0c8v2zqStEOHTpI37595ZprrrH60nCedFmzrhLSolutYdEpIV+OsBBaCC0AEBDabkJHVTZu3CirVq0yLSwAf7x/W75hIgDAmXQ05cUXX5SaNWvK+++/b0LL4sWLrb4sBDFCCwCgQHSAftasWVKrVi3Td0VbV2gdom69wlQQ/IkxPACA13T4/vbbb5evvvrKHGs/rddee03uvPNOljDD7wgtAACvab2BblarHcl1+bIuYy5evLjVl4UQQWgBAORJe6zoBobt2rUz/bN0NGXChAkSGRkpVatWtfryEGKoaQEA5Grp0qVy5ZVXSpcuXeSFF17wnK9RowaBBZYgtAAAsvnjjz/k3nvvNfvBrVixwkwJVa5c2erLApgeAgD85eTJkzJ69GgzqqKN4pTuEzRkyBApV66c1ZcHEFoAAH/R5cu6Wa3SaaExY8ZIw4YNrb4swIPpIQAIYVmboj/yyCOSkpIi77zzjnz//fcEFtgOIy0AEIIyMjLkpZdeMpvUfvDBB+ZcUlKSbN68WcLD+X0W9sS/TAAIsZGV6dOnmxVAw4YNk5kzZ8qPP/7ouZ/AAjvjXycAhAhts9+kSRO5++67zQohXbb86aefyhVXXGH1pQFeIbQAQAi03u/evbs0aNDA9F6JiYkxK4J0g8M2bdrQfh+OQU0LAAS5qKgos1eQTg116tTJTAtVrFjR6ssCCozQAgBBaMmSJdKoUSOJiIiQYsWKyeTJk81+QU2bNrX60oBCY3oIAILIjh07pH379iacTJo0yXP+uuuuI7DA8RhpAYAgkJmZaaZ9tDnc8ePHzQjLzp07rb4swKcILQDgYFqn8tFHH0m/fv0kLS3NnLv22mtNO/5LL73U6ssDfIrpIQBwMA0rd9xxhwksF154oXz44YeyYMECAguCEqEFABysc+fOUrx4cXnuuedMN9vbbruNJcwIWkwPAYBDnDlzRiZOnCgHDhyQp556ypyrX7++GWVJSEiw+vIAvyO0AIADfPfdd9K7d29Zs2aNREZGmimhatWqmfsILAgVTA8BgI39/vvvpiFc8+bNTWApWbKkjBgxQipXrmz1pQEBx0gLANiQLlvWcKI7MetyZq1T6dGjh7z44otSunRpqy8PsAShBQAK6MxZlyzffkD2ZByXsrHR0rBKokSE+7b4VetWdH8gDSy6yeHYsWOlXr16Pv0zAKchtABAAczf8Ic8P2eT/JF+3HOufHy0PNumlrSsXf68nlubwV1wwQXm8woVKsjw4cMlNjZWOnbsyIoggJoWAPB+dGX01z/L/dNXZQssanf6cek1fZUJNIXdhVn7rWidysKFCz3ne/bsaepZCCzAXwgtAHAOGkaavLxARn69Ndf7Xf/7qCMwGm68dfbsWZkyZYqkpKSY+pXTp0/L3LlzfXTVQPAhtADAOQKLjqLsPnwi38dpVNERGK118cayZcvMLsxdu3aVPXv2mODy+eefmykhALmjpgUA8qCjJjp64v3YiZji3HMV7s6e9JoMe/llc5/WrAwaNMj0YImKivLh1QPBh9ACAHnQoJGzfuVcNJScq3A36r9//ei97777ZOjQoZKUlOSjKwaCG6EFAAowapIXLZVNiv9r+XPOqaXMX1eK69QJialxlTl/8sIrpUKXsXLno+0JLEABEFoAwMtRk3PRZc/ufi06JTRg6lfy5+xxcuyX5RIeEy/RlepIeHQJkbAwiSpbxYzAXF8ryec9XoBgRSEuAL/TN/Cl2/bL7DU7zceCrLCxko6aaA+Wc0UKfcz4uy739Gk5cuSIdH3wUVkz6p8msEh4hBS/5FqRsPBCF+4CYKQFgIObsfmbjoDodeoUjwaX3KLWI6nV5aEW1c1jXS6XvPfee9K/f3/ZtWuXuT+6cj1JvK6HRJZOPu8pKCDUMdICwG/cNR2+bsYWSBqsdBRF61Wy0uD15l2XS5/UFM/0zvr166Vz584msFRIriRl/jFQyt7xQp6BpTBTUEAoY6QFQMCXC+s5fZt3Sk2HBhe9ztz2Gzp58qRnqXKdOnXkwQcflPLly0vfRx6V1NHfm4Dm8rJwF0D+GGkBYMlyYXdNx8ivtjqizkUDSuOLSsktdS8wH11nz8jrr78ulSpVkm3btnkep+eefvppKR5TzEwtqZyRLCyXwl0A50ZoAeAX3tZqvP7NL9Jx4g/SdNhCR0wXKd0fSHdcfvjhh2X37t0mqBRkaikpR+EuAO8wPQTALwpaq+Guc7Hbm3nWbrZnDu+VGWNekn//+yNzX2Jiorz00kvSvXv3Qk0tASgYQgsAvy4Xzqumwwl1LllXPqUv+0jSF88Q1+mTEh4eLr169ZIXXnjBBBdvp5YAnB+mhwD4dbmw8jZ+2Kl3Sc6VT67Tp0xgib7wUkm6d7S0vv9prwILAN8htADwm7xqOs7F6t4lOiX0xMS5cnz3L55z8Ve2lzK3PiVlOwzxdLO1e/EwEGwILQD8HlwWP9FC3uveSB669iKvvsbK3iUHDx6UDl16yrqxPWX/56PNKiEVViRKYlKukrCwMFuNCAGhhNACwO/cNR2PXF8j37b4er68Rb1Lzpw5IxMmTJDq1avLR/+aKOI6K5Ely4vr5DHbjggBoYZCXAC2aItvZe+SJUuWmOXLq1evNsdVqteUo5ffJcUq18336+hmC4TQSMuhQ4dk2bJlZjgWQGiwW++SxYsXS9OmTU1giY+Pl9GjR8um9Wul6mWNbDkiBISyMJfu8GWBDz/80PQ2SE5Oll9//VWmTp0qt99+u2zYsEG6dOkiv/zyi3Tr1k1eeeUVM4fsjcOHD5sfOunp6RIXF+f3vwMA3/Q/sbJ3if4IvPrqq6VmzZqm50qZMmWyrR4yj8nyePcV2q2fDOBk3r5/WzLSohf1wAMPyKJFi8wGY+PGjTO7op44cULatGkj9evXlxUrVsimTZtMmAEQfHK2xQ9EYNGAMmfOHGnWrJn5Ian0l6IFCxaYehZ3YLHjiBAAi0Za0tLSTGDR3VDVunXrpEmTJjJt2jTp2rWr/P777xITEyNr1641m4/p8K03GGkBkJctW7ZI3759Zf78+eZ40KBB8vzzzztmRAgIZt6+f1s2PeR26tQp6dGjh6ncv+iii0yNy7x588x9emmlSpWSAwdyX1aoIzN6y/qX1ukmQgtCAW+m3tGfCy+++KKMGjXK/LyJjIyURx991GxqGBsba/XlARDvQ4ulq4d0JKVFixZmW/fNmzfL4MGDpUqVKp77ddg2IiLCFOomJCT87euHDh3q1W9KQLDJ2l7eTQtDdeUN0xb/T0dvH3/8cbOpoWrVqpWMHDnSLGsG4DyWrh6qU6eOfPnll+YHiBbdFilSRIoWLZrtMdHR0ZKZmZnr1w8YMMCkMvdNp52AYJezvXzODQedslNyIOjPFw0s+jPms88+MzcCC7KOVi7dtl9mr9lpPtLh2P4sHWnRkRQtun3nnXfM1JCOnOjqoawyMjLMSExuNODkDDlAMNMfqjrC4nLIhoOB9ueff5qp5goVKpjjYcOGyWWXXSa9e/fO8+cIQhOjlc5kyUjLf/7zH7NayE1/mGiAufjii2Xp0qWe89u3bzc1K2xKBvxFa1hyjrBkFart5bVWRad9UlJSTEBx0/Dy2GOPEViQDaOVzmVJaNEfLLq8UG86pfPUU0/JDTfcIDfffLMpxpkyZYp53JAhQyQ1NdXUtQDwvm18KLWX1ykgnWrW4lr9+bFjxw45cuSI1ZcFh45WKjbDtC9LQkv58uXlo48+Mp0nL7nkElOz8q9//cvUtEyaNEkeeughKV26tMyePdsM7wIoWNv482kv75R5fm1K2a5dO7nxxhvlp59+Mj1W9OfH8uXLpUSJElZfHmyK0Upns6ym5frrr5eNGzf+7Xzbtm1l27ZtsnLlSmnUqJFZ8gzgL7qsWefddRg7tygR9r/mZ4VtL++UeX5tBqcrgXT6WEdidUpI+66ULFlS7I6l6tZitNLZbLlhYlJSkvmBBCBwGw665/lzhiH3PL+dusA2btxYypYtKzVq1DAjtrVq1RIncEooDGaBGK1EkC55BlBw/mgvb/d5/jVr1kivXr3MyiClHbO1EaXWszgpsFD8aZ/RSjbDdCZbjrQAyJ8GE13W7KtphoLM8+s+QYGyf/9+eeaZZ+Stt96Ss2fPmhYJ2tPJXRvnFCxVD43RSvgfoQVw+IaDvqiv+NzL3/IDNc9/+vRpE1Q0sGhHbHXnnXeaolsnskMopJbm76OVOafqdLSSqTp7I7QAISq3+go7zPN/++23prBWd4BXupx5zJgxcvXVV4tTWV38SS2N/0crERiEFiAE5VV0m5+SMZF+n+fXTVJ1ew4NLNpUUjc67N69u2mH4GRWFn86qcDaiaOVCCwKcYEQk199RX789fvnsWPHPPuLaWdsHVXRotutW7eaj04PLFYWf9q9wBooKEILEGLOVV+Rl4OZp3zacEtHVT755BOz+kdHVNwaNGggb7zxRlD1aHIXf6qcwcWfxZ80UkOwIbQAIeZ86iZ8VXOhjSW1weQ//vEP03Z/5syZcvLkSQlm/liqbvdaGsDXnD/uCqBAzqdu4nxrLg4dOiTPPfecvP7666bniu7SrpunPvnkkyGxqWGgiz9ppIZgQ2gBQsy5tgIQP2wPoBYuXGiWLe/bt88c675Bw4cPl6pVq0ooCWTxp7+3fQACjekhIMTkV1+RG1/VXFSvXl2OHj0qNWvWlC+++MLUs4RaYAmVWhrAXwgtgI35a8flvOordFmz3nxRc7Fr1y4ZP3685zg5OVm++eYbWbdundxwww3n+TeAnWtpAH8Jc2kJf5A4fPiwxMfHS3p6usTFxVl9OYDtG4Ll1iVVnU/Nhe68rJsYDh48WI4cOWKaxTm5MVywoCMuguH9m5oWwIYC1RAsr/qKwtZczJs3T/r27Ss///yzOW7UqJH5QQTr0UgNwYDpIcBmnNgQTENK69atpVWrVubzpKQkeeedd2TJkiVSt25dqy8PQJBgpAUIkc31/DU9oEuXW7ZsKb/++qtERkaakZaBAwcyRQvA5wgtgM34oyGYr+tjzp49az6Gh4dLRESEqV+ZPn26jBo1SlJSUgr8fADs7YxNaqIILYDN+LohmK/rY1asWGF2Ye7Ro4fcd9995lzHjh2lU6dOXj8HAOeYb6NdwqlpAYJ4cz1f1sfs2bNHunXrJg0bNpSlS5ea/YJ0ashcUxirUIBgNP9/v/TknLJ2/9Kj9wcSoQUI4oZgvtgw79SpU55pn8mTJ5uNDu+66y5ZtGiRmRoCEJzO2HBRQIFCy5tvvumZy86LbnpWrVq1870uIKT5qiHY+dbH6IjKZZddJo888ojpn3D55ZfL4sWLZdq0aVKhQgWvnhuAMy234S7hBappGTJkiHTv3l0+/vhj0whGi/By0t/CTp8+7ctrBEKSLzbX80V9zObNm6V06dLm+79r166MrgAhYo8NdwkvUGgpUqSI+YE1dOhQueKKK+T999+XDh06yIcffii3336752NuYQZA4BuCFXTDPN0baNmyZdKiRQtz3LhxY7Mq6Oabb5aEhIRCXwcA5ylrw13CvU4Xx4//f5LSojvdU0R/+9KP5cuXz/YxiHYGAGy1Z5C/6mN08OaDDz4wmxlqQNm+fbvncZ07dyawACGooQ8XBQR0pEV/+ypTpoyZ9rnqqqs8LbrdKwZyfgRCuYfBjn2Z8t7y32T3YeuXB2atj8m5ZDHpf9dU/sweueaaDqawVlWqVEn++OMPqVKlSsCvFYB9RPzvlx5dJaTv7i4b7BLuVWiJioqSTz/9VO655x558MEHTbdLAHn3MBA/7xnki/qYi+Jc8vxzz3oK7KOjo2XAgAHSv39/KVasWMCvEYD9tDzHLz2B/nnmVWjR1typqanmh5oOFevyxwkTJphiXP148ODBbB8ZcUGoyKtxW056v35X6De+hgcrOklmrY/RnZgvuugi2blzpzlucdMtMnHcKKlapXLArwtA8C8K8JUwVwEKUC688ELZsWOHPPPMM/L777/nWXCrW9FnnRO329bWgK+mhJoOW5jvCEtu3uveyGe77Ra2tbaGrW59npA96xZJYmpPia5Ux9IpLACh7bCX799erx6aM2eOvPDCC+bzl156Kc/Had1LcnJyQa8XCLoeBgVZHliY8FGQ1tppaWny+OOPy0MPPSQZ8VXN6FBEvX9I+fq3SVh4hC2msADgXLwKLTrtc++990q5cuUkMzPTbDufX3O5+++/35unBRytsL0Jci4PLMy+Ht7uJ6Sr/l597TUZMmSoHD+WKWs2/CRxHV79a7qqSKTtprAA4LxDiy531NUEs2fPNqMsGzZsMAFGe7XknF3SvUi07TcQ7AramyBnT5TCbmZ4rtba+uc89+lGOfbzMnng4T6ye+dv5r6iFWtJ+hVd5FjGCa86XPpqCgsAfMXr6aGiRYvKHXfcYW66/8hjjz0mJUuWNCsPYmJifHZBgFOcq3GbnGN5oDfhI7dRj3NNS53cnyZrP5gg/9ix2hxHlCglCdd2lZiLm3tdJB/IDpcA4K1Cta795z//KatXr5b69esTWBCy8mvcllNuewYVdl+PcwWKE39sleM7VktYRKTENb5DKnR/U4rXurpAq/oC2eESAPzSxj+rypUrS58+fQr75UBQ0BAyrtPlMnD2Bjlw9KTnfHx0Ebm+VjlpUr2MJMXlXlhb2H09cgYKl+usnE7fI5El/6o1K37JtXJqX5qUuOxGiUwof95TWABgF16Flk2bNsnLL79s9h46F/1tTnu6dOzY0RfXB9ia1qQMnrspW2BR6cdPy0erdsqSbfvz7Bjp7WjGvowTZirJ/RxZp6WO7/xJDix4S85k7JcK3d6U8KIxEh4WLlVu6i6HjhWstsyqDpcA4NM+LdqA6t///repazmXP//8U4YNGyaHDh0yTekCiT4tsFtjOfdbf14FtdrnxZuamJyriWZ8u1Z6PNxPjm5Y8NefE1VMyt72rBRLrm2O+6ZWl5Ff/7Xdhrfo0wIgKPq0XHDBBdK7d2/55JNPZO7cuX9rKqe9WXTF0LRp08ySZ10WrceBDi1AoORXROttQW1++3pIHquJxtxZW7Ys+ND0TDqakWHuK35pqiQ0v1ciSiR4Wmvrn/X+j2n57u5cLq6oDL+jruw7csLSDpcA4JeaFu2I26RJk7+FFt23RIOL0qAyaNAgCnQR1ArSWC6/ZcR57euR23O4TmTKHTc0k+P70sy5hg0byqjRY0TKVMu1Kd25Njp7ru0l0qRa6QL/3QHA9qFFN0ycN2+eCSzuGSWtX9GwovuYTJo0Sf773/+avYn0h+mIESP8ed2ApQqzJDivr3Hv6zF1yXYZPHdznl8fpvUqZapKopyQ4a8OMxuY5rWVhh03OgOAgIWW9evXmymf5s1z7/Xw66+/ytVXXy3dunUzexMBwawwS4Lz+xodHSkdm71m7OzJY5L+w4cSW7elFIkra84lpvaQYbfXk07NLnbcRmcAENDpIZ0a0mmf7t27m4IZ3b6+TJkypuZl//79ZnSlU6dO531RQLA1ltPRjfqVEmTptv15hgd3qNGRzKObvpVD306RM0cOyOmDf0iZW54w90XExEuVCn8FmMLs7gwAIdWnRZcza2O5qKgo07J/z549snHjRlOgq7fly5fLiy++KCVKlPDPFQM2UJAiWr2vZvlYaTR0Qbal0TlX62iIiT3ym/w8a6yc2PnXNFGRkklSvNY1BQo/ABDSS57V0KFDTa+WnMuetaZFN2XTehZd5vzggw+aJdLuYBNILHlGoOW22aG3si6Hrl+uiDz99NOmNky/JcMii0p84zslrkE7CSsS5Xlsj+ZV5NO1fxRoc0UAsDtv37+9Di36sB9++EEaN27sCSvuZnPvvPOO2ZNIp4t09GXWrFnSvn17CTRCC6xa/uyuGfl171EZvcD7/iju0ZPrjy+SwS+8YM5dc/Otkn7pHXJAYrMFk7aXlZcJi7b/bVQnv14wABCSoUWLaz/77DNZtWqVCTDt2rWTFi1aSNu2beXOO++UtLQ0eeKJJ6Rnz56WLXcmtMBK7mZx3o66nD15XMKj/qpjebvzpTLyqQfN91DTpk2zBSGdAtIpoatf/SbP53aHn8VPtGCqCIDjePv+7dWGidosTkdPPvroI7NyaODAgbJ3715TkFu1alX58ccfTcdcXRJdsWJF02QOCDXe9m45nf6n7P1kiPz5/lNm3yCVcSZC5syZYwJL1uLZW+peYD6u/O/BQm2uCAAhV4irIyfr1q3zLHXWsKIdcosXL55tZdFXX30lU6dONSMwQKg5V++Ws6eOy+Ef/i2Hl/9bXKdPioSFy8ldW6XoBTXPuYS6sJsrAkDIhZZjx45Jq1atZOHCheZYp4i0C25ERIQ51s+1GPfWW281t2uuucZMJelSaCBU5BU8dDo1c8sSObhwspzJ2GvOFb2wjum5ElWmspQsFilnXa5smyJ6+9yFfRwABG1oiY6Oln379nmOH3/8cbnqqquyPUZHYW644QYzCnPLLbcQWBCyvVuyTuOcOXZY9s4aKid+W2+OI+LKSMK1/5SYGk08I5e6G3PnScvyXQV0rr4w7poWfRwAhHRo0R+u7pVC7umit99+W44ePSolS5b0nJ85c6ZZPfTcc8/552oBh/RucQeL8OgS4jp53CxbjruyvbmFR0bnuylibquA8usL4x6b0fspwgUQzLwqxFXbtm2T++67zzSO08LctWvXSqlSpSQxMVFq164tt99+uwkx7777rlfPN3v2bFPEq2Gobt26snnzX820NmzYIA0aNJCEhATp37+/Z58jwAmuv7is3Fp8q5Qt5t6fK1xK3dxXKnQbLyWbdpZS8SWkeNRf06o5uf+la98XnSrKay8hHVHJSo9Z7gwgFHi95Lly5cpm1dCff/4pEyZMMM3klAYYXUmknXDHjRtn+rd88cUX2Yp0cwtAGkzefPNNs1/Rww8/bBrSac1MzZo15cYbbzSBRYt9b7vtNunSpYtXfxmWPMNKS5YsMf+WtbHiU08/La27PCK704+ZLriJJYpKUly0qV3RqaBzea97ozxb7+dcDk1HXABO5+37t1fTQzrlo235dTNEpV07c1shNGrUKDNt1LVrV/nggw/yfD4dVdHuutqQTvXq1csU+n7++efmgnUPI52CGjJkiOmwm1do0d2l9Zb1Lw0EmgZurfOaMWOGOdYp0+SKFXMNHbPX7DzvVUDsJQQgVHkVWk6ePJmtsFb7sGidi64q0kSkq4c0PFSpUkVGjhwpl19+uendoqMpuWndunW24y1btkj16tXNlFOjRo08zenq1KkjmzZtyndrgeeff97bvyvgU7piTv+9v/TSS2ZqVL8ntBBdp1B1I9HcsAoIAPwcWrQ9v9adbN26VVJSUjwNsLQe5ZJLLpH33ntPatWq5Xn85MmTTXDxNhANHz5cHn30Ufnll19M8HHTNwFdVn3w4EFT45LTgAEDzNdlHWlJTk726s8FzpdOYb7++uvmc11NN3bs2HP+u2cVEAAEoBD3ww8/lObNm5vpHw0a+lvmxRdfbDrh5gwKV1xxhYSHe/fUzz77rKl/0aknDUE5N2TU5dZaN5MbfayO9GS9Af6UtQTssccek2rVqsn06dNl8eLFXgV19yoglbMKhVVAAOCj0KIFMt9//72naZwOiWtBrdalaDGt/sDWW8OGDeWtt97y6jm18FaLd7UWIDIy0qxE0qLerDIyMgK+WzSQk47iaUjRei23SpUqmanNzp07e3queINVQADg59VDOqqiBbR9+vQxK4h0abOe0/1SchYl6g/2HTt25Pt827dvN/Urr732mtx9992eENOjRw8zTeR+jE47HTlyxNN9Nz+sHoKvab2W7mKuU5H6716tX7/eLPM/X6wCAgA/rB5Shw4d8hS/6siHrijS3y5r1KiR7XEVKlSQe+65J9/n0gJeLcbVzrna9l9DiWrWrJm58ClTppgVQ7p6KDU11avAAvjasmXLzLJ7Xc6vtJ5LV8j5IrAoVgEBgJ9GWnL7DVT7UdSvX7/AX6uN5dq1a/e38zqyohszduzY0RT/al3Mt99+m63INz+MtMAXDhw4IP369TObf6rY2FgZNGiQCTBMVQKA73n7/l3o0OJPu3fvlpUrV5rpI+266y1CC3xB66h0VEX/HWoXaB1dTEpKsvqyACBoOTq0FBahBYWlq3902bJ71Zs2OtRl9hqcAQD2eP/2evUQ4GRa9Lp0237TkVY/uvf20RVwWlul9VRZ98266aabCCwAYDNeF+ICTjV/wx9mE8I/0v+/NX7ZaJdU3fWVfPLOm6bvkPYISktLs/Q6AQD5I7Qg6ANLr+mrPN1ndTY0c/N/ZNU3b8uPRw6Yc9dff72MHj3aLOEHANgXoQVBS6eAdIQla9HWgS/HyZE1883nReLLSdU2D8i8qQOlSAQzpQBgd4QWBIXcGrXpcdYpIVX8kmvl6MZvJL7RHRLX8FY5USRKftxxkH4pAOAAhBYEZc2Kbkp4w8WlJWPVZ+I6c1riGvzVFyi64iVyQa+pElEs1vNYDTq5oWMtANgLoQVBVbPitn39chk6coKc2rtDwopESUyNJlIkroy5L2tgURpIvA1CupkhewMBgDUILQiqmpXTh/fIwYVvS+aWxeY4PDpWSja/WyJKJP7t68P+t0mhjqB4E4R2px8359nUEACsQWjxM6YY/CdrzcrZUyfk8PKP5fAPH4nr9AmRsHCJrXeTxDe9y4ys6P/xrCHE/QroyEnW1yO3IOSm5/SRev/1tZJ4HQEgwAgtfhRMUwx2DF9Za1HOHD0o6Utnipw5JUWTa0tiak+JKlvF3PfPJpVl3obd2V6HpDxeh9yKd3MGF71fH0fxLgAEFqHFT4JpisGu4Sss86Dn88iSSZJwzX0SUTxRYmo2NTuQu6XWSpKnWtXyKnTlVZRb2McBAHyH5hR+cK4pBqX3u1vJOyF85Rx9cIcvvT/QDh48KH369JH2V9eTEum/eqZ64q64RYpf3MwTWML+F67cAUVHRm6pe4H5mNcoUW5FuefzOACA7xBa/KAgUwx2ZrfwdebMGZk4caLZgXnMmDFy+vRpufTMNnNfbhFEr6pDg+QC/RkacDTo5DXxlTUIAQACi9DiB8EyxWCn8PX9999Lw4YNpUePHrJv3z6pVauWfPXVV/LehFFmqk1rVHIz8uufpemwhV6PCOkIjE57qZzBJa/iXQBAYBBa/CBYphjsEr4effRRadKkiaxatcpsXT5q1ChZs2aNpKammvu1rmbxEy3kkdSUXL++oFNZ+ny5BSE9dlItEgAEGwpx/cA9xaBvlrlNnOTVH8RuduzLtEX4uuSS2qZOJbVdB+nz5CBpeUWNXEc63v/xt1y/vqBLlXW6K75YlDx+Yw05cPSkJJYoKklx9lgxBQChjNDiB+4pBv3t3tv+IHajoxKjvt6a72P8Eb50F+a5c+eakNKqVStzHRN3J0tSl7GytUxlefDjbVJ+wc6/rVzy1VLl/FZK2fn1AoBQwPSQnzh5iiG/Atys9H5fvplv2bLFBJU2bdpIz549ZdbybSb47c44KVFlKuc73eOLqSw7rpQCAPw/Rlr8SIOJTkfYrSnbuZxr1MLtkdTqBQpfeTWoO3z4sAwePFhGjx4tp06dksjISOnUubMM+XyzuHJZx5PbdM/51hHRCRcA7I/Q4mfu/iBO6mjr7ahF5dLFvf7zc5t2SYqNkqtcm+Sd0UPkwL495tzNN7eSUaNGyr7wRJk58Qevp3vOt46ITrgAYH+ElhBQ0I62vl79lFd34P9uWS/LpvUznxdJqCAJ13WXA5c3k20nSsiJ0wWb7jnfOiK7rJQCAOSNmpYgV5g6DV82WMs57eI6c8pzX9EKNaREnRuk5DX3SYWu4yTmogae69qx72iBg5NO3fRNTZH4YpEFriMKlmXqABDMGGkJYoWt03CPWtw/fVWhCnCzTkXtyzhhApPrzGnJWPWZ2Yk56e4RUiSutHlsqZt653pd7y3/zSwz/vOwd9M9uY0mlSwWKV2aVJaHWlQ/Zx1KsCxTB4BgRmgJYlbUaeQWHo5tXy0HF0yQU/vTzHHGmnmS0PyefK9r9+ETplmcLrs+13RPXtNP6cdOyaivf5YaSbHnLBgOhmXqABDsmB4KYoWt03CP0OQlLI89h3JORZ06tFv2fPyi7Jn5jAks4cXiJLHlw1KyaWevrqty6ZhzLhv35f5ITl6mDgChgJGWIFbYOo3CjNDkDA+Hlrwn6UtnimgNS1i4xF7eWko27STh0SUKdP36/PktG/f1aJJTl6kDQCggtASxwtZpFGaEJmd4cJ06YQJLdKXLJOG6HhJVppLX153zuvJbNu6PVT92WaYOAMiO0BLEClunUZgRmmUrVsnJvTs8nWvjr7rTrA4qVr2RacnvrYLWj7DqBwBCBzUtQa4wdRoFWfK8f/9+efDBB6Vfp5ayf/5YcbnOmseERxWTmJTGfwssz7S6WEZ3qCvvdW8kb3SqZ57H2+vKjS+XZwMA7I2RlhBQ0DoNb0ZoBt6UIm+9OV6eeeYZOXDggDlXolSSyMnjIkVj8pzyua9JlWx/7o21y59X/QirfgAgdIS5dFvdIKF72MTHx0t6errExcU5pm2+0zrp3pqULu+Oel7WrVtnzl166aUyduxYOVYqxYQHySM8+HMFTkG7/gIAnPf+TWjxg2B6A80ZvtJ/WSk3tbzR3JeQkCAvvvii9OjRQ4oUKWL53z0YgiIAhKLDhBZrQktejc4CMdoQCGfOnJFGjRpJgwYNzM7MpUr9fZUN4QEA4I/3b2pabNA2vzB/TiBCgebZWbNmyZgxY2Tu3LkSExMjERERsmTJEomKisrz61gyDADwB0KLw9rmB2r6ZdOmTdKnTx/5+uuvzbHWrDzxxBPm8/wCCwAA/sKSZx/yR6Oz892xuaAOHTokjzzyiNSpU8cElqJFi8rAgQPloYceOu/nBgDgfBBafMifjc58ucdOXlNBkydPlpSUFBk1apSpXWnXrp0ZcdHaleLFixfqeQEA8BVCiw/5s9FZQaaeCkObwGndyt69e6VmzZryxRdfyCeffCJVq1YVO9AwtnTbfpm9Zqf5WNhwBgBwLmpafMifjc78MfW0a9cus1S5bNmy5nj48OHSrFkzMxUUGRkpdhFMS8gBAIXHSIsN2uYHeurpxIkT8sorr0iNGjWkf//+nvNVqlQx9Sx2Cyz+ruMBADgDIy02aJvvzx2bc5o3b5707dtXfv75Z3O8detWOX78uERHR4fsEnIAgDMw0uIn7l4lt9S9wHw83zdV99STyvlM3kw9aThp1aqVuWlgKVeunEydOtX0XLFjYAlEHQ8AwFkILSEw9aQFtrVr1zajLFrD8thjj5kQc++990p4eHjILiEHADgL00MhMPWkxbWJiYlSr149GTlypFkdFOpLyAEAzkNocaBztclfuXKlvPPOOzJ69GizlFn3cVi1apWUL1/eHDuFr+p4AADBwb5zAygw7bGiOy7rZobadv/999/33FehQgVHBRZf1PEAAIILoSUInDp1yoyqVK9eXSZOnGi623bu3FmaN28uTuevJeQAAOdhesjhFixYIL179zbt9pXWregoS5MmTSRY+GMJOQDAeQgtDnb27Fl59NFHTWApXbq0DBkyRLp27SoRERESanU8AIDgZ9n00L59+0wH1h07dnjObdiwwdRjJCQkmE6tOs2B7DIzM00zOKXLlceMGSMPP/ywWcLcvXv3oAwsAABYFlo0sLRu3TpbYNHW8m3atJH69evLihUrzOiBNj/DXzTAffjhh2a5su4R5Hb11Veb4KJBDwCAYGZJaOnQoYN06tQp27nPP/9c0tPTZcSIEXLRRReZqY7JkydbcXm2s379emnRooXccccdkpaWJjNmzJDTp09bfVkAAAR/aNEVLlo8mtXatWulUaNGEhMTY47r1KnjKS7Ni47OHD58ONstmBw4cMDsuFy3bl359ttvTbv9559/3oxEaWdbAABCiSWhRWtZctLAkfW89hTR+oyDBw/m+TxDhw6V+Ph4zy05OVmCxfz5880S5nHjxpmC29tvv11++uknGTRokBQrVszqywMAIHT7tOjIQdGiRbOd05EFLTzNy4ABA8yUkvumUyfBolq1anLkyBGzZ9DChQtl5syZUqlSJasvCwAAy9hmjkH3xtHVQ1llZGRIVFRUnl+jISdn0HEqDVw6uqIrgNyhRaeEdDUVU0EAANhopEXfnJcuXeo53r59u6lZ0TATzHT58ksvvWRWBWkL/uXLl3vua9y4MYEFAAC7hRZtOa91LVOmTDHHunooNTU1aPuO6BLm2bNnS61atWTgwIFmGqxp06aeQmQAAJCdbX6N1xGFSZMmSceOHU1jOW2cptMjwWjz5s3St29f+fLLLz2bGb722mtmKbjTNjUEACBQwlw2azu7e/duWblypVn+XKpUwdq260iNriLSoty4uDix6+aGlStXll27dpl6nX79+slTTz0lJUqUsPrSAACwhLfv37YLLefDrqFFlyzrCIp7FEX71MyZM8c00tOCWwAAQtlhL9+/bVPTEqyWLVtmRo0++ugjz7lu3brJp59+SmABAKAACC1+nObq0qWLCSw//vij6WTrHtSibgUAgIIjtPjYyZMnTVFtSkqKZ8PH++67T77++mvCCgAAwbB6KBgsWrTINIfbunWrp/fM2LFj5corr7T60gAAcDxGWnw8yqKBpWzZsvL222/LDz/8QGABAMBHCC3nQfcG0tEVN22Gp2FFg4vWs2ivGQAA4Bu8qxaCFtTOmDFDatSoIa1atTI9V9w0rOiyLQAA4FuElgJavXq1NGvWTDp37mzCik4F7dy50+rLAgAg6BFavLR3717p2bOn1K9fX5YsWWL2CNL9kTZu3GgKbgEAgH+xesgLR48eldq1a8uePXvMcadOnWTYsGFSsWJFqy8NAICQwUiLF4oXLy733HOP1K1bV7777jt59913CSwAAAQYew956dixY2aDw4iICJ8+LwAAoe6wl+/fTA95qVixYlZfAgAAIY3pIQAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4AiEFgAA4Ai2DC0bNmyQBg0aSEJCgvTv319cLpfVlwQAACxmu9By4sQJadOmjdSvX19WrFghmzZtkqlTp1p9WQAAwGK2Cy2ff/65pKeny4gRI+Siiy6SIUOGyOTJk62+LAAAYLEiYjNr166VRo0aSUxMjDmuU6eOGW3Ja1RGb26HDx8O2HUCAIAQH2nR4FGlShXPcVhYmERERMjBgwf/9tihQ4dKfHy855acnBzgqwUAACEbWooUKSJFixbNdi46OloyMzP/9tgBAwaYqST3LS0tLYBXCgAAQnp6KDEx0aweyiojI0OioqL+9lgNNzkDDgAACE62G2nRpc5Lly71HG/fvt3UrWiYAQAAoct2oaV58+amrmXKlCnmWFcPpaammroWAAAQumw3PaQ1LZMmTZKOHTuaxnLh4eHy7bffWn1ZAADAYrYLLapt27aybds2WblypVn+XKpUKasvCQAAWMyWoUUlJSVJq1atrL4MAABgE7araQEAAMgNoQUAADgCoQUAADgCoQUAADgCoQUAADgCoQUAADgCoQUAADgCoQUAADgCoQUAADgCoQUAADgCoQUAADgCoQUAADgCoQUAADgCoQUAADgCoQUAADgCoQUAADhCEasvwO7OnHXJ8u0HZE/GcSkbGy0NqyRKRHiY1ZcFAEDIIbTkY/6GP+T5OZvkj/TjnnPl46Pl2Ta1pGXt8pZeGwAAoYbpoXwCS6/pq7IFFrU7/bg5r/cDAIDAIbTkMSWkIyyuXO5zn9P79XEAACAwCC250BqWnCMsWWlU0fv1cQAAIDAILbnQoltfPg4AAJw/QksudJWQLx8HAADOH6ElF7qsWVcJ5bWwWc/r/fo4AAAQGISWXGgfFl3WrHIGF/ex3k+/FgAAAofQkgftwzL+rsslKT77FJAe63n6tAAAEFg0l8uHBpPrayXRERcAABsgtJyDBpTGF5Wy+jIAAAh5TA8BAABHILQAAABHILQAAABHILQAAABHILQAAABHILQAAABHILQAAABHILQAAABHILQAAABHCKqOuC6Xy3w8fPiw1ZcCAAC85H7fdr+Ph0RoycjIMB+Tk5OtvhQAAFCI9/H4+Pg87w9znSvWOMjZs2dl165dEhsbK2FhYUGVQDWIpaWlSVxcnNWXg1zwGtkfr5G98fqE9mvkcrlMYKlQoYKEh4eHxkiL/kUrVqwowUr/kfDNbG+8RvbHa2RvvD6h+xrF5zPC4kYhLgAAcARCCwAAcARCiwMULVpUnn32WfMR9sRrZH+8RvbG62N/RW3wGgVVIS4AAAhejLQAAABHILQAAABHILQAAABHILQACEqHDh2SZcuWycGDB62+FAA+QmixoX379kmVKlVkx44dnnMbNmyQBg0aSEJCgvTv3/+c+zPAf2bPni1Vq1aVIkWKSN26dWXz5s3mPK+RfXz44YdSuXJl6datm2k4qceK18ieWrZsKVOnTjWf/+c//5GLL75YSpcuLSNGjLD60kJa7969TXd5961atWqWfx8RWmwYWFq3bp0tsJw4cULatGkj9evXlxUrVsimTZs83+AIrG3btkmXLl3k5Zdflp07d0pKSop5Y+Q1so/09HR54IEHZNGiRbJ+/XoZN26c+cHKa2RP7777rnzxxRfm871790rbtm2lY8eOsnTpUnPfN998Y/UlhqwVK1bI3LlzzWil3lavXm3995EueYZ9XHfdda7Ro0drbHVt377dnPvkk09cCQkJrqNHj5rjNWvWuJo0aWLxlYamOXPmuN566y3P8cKFC13FihXjNbKR3377zTV9+nTP8dq1a10lSpTgNbKh/fv3u8qVK+eqUaOGa8qUKa6RI0e6atas6Tp79qy5f9asWa7OnTtbfZkh6dSpU664uDhXRkZGtvNWfx8x0mIzEydONENyWa1du1YaNWokMTEx5rhOnTom3SLwdBSsR48enuMtW7ZI9erVeY1sRDd069y5s/n81KlTMnLkSLn11lt5jWyoX79+5rXR10Xpa3Tttdd6Nrxt2LChrFy50uKrDE3r1683mxDrFHixYsXMFN5vv/1m+fcRocVmtJYlt501s57Xb+iIiAgKDC128uRJGT58uNx///28RjakP1yTkpJk/vz5MmbMGF4jm9FpnwULFsgrr7ziOZfzNdJN+Xbt2mXRFYa2TZs2SY0aNWTatGmybt06U8Onv7BZ/X1EaHEA/ceSs21ydHS0ZGZmWnZNENPOunjx4qamhdfIfvQ3wC+//NKMhPEa2cvx48elZ8+eMn78eImNjfWcz/ka8fpYp3PnzqZmpXHjxuZ76I033pCvvvrKjL5Y+X1EaHGAxMREU6CWVUZGhkRFRVl2TaFu4cKFpsBzxowZEhkZyWtkQ/oboBYLvvPOO/Lxxx/zGtnI4MGDzeqTVq1aZTuf8zXi9bGPsmXLmsCio5dWfh8RWhxAv7m1kt5t+/btpoJbv8ERePr/X1c3aGipVauWOcdrZB+6ZFZXC7npD1MNMLqMltfIHjTsa+uAkiVLmpse64ovDZhZXyNdrXLBBRdYeq2hqn///uZ1cdPXJTw8XC699FJrv48CVvKLAsm6ekiruMuUKeN6++23zXG3bt1crVu3tvgKQ1NmZqarVq1aru7du5uqevft5MmTvEY2sWvXLrPqQVd56Uqie+65x9WyZUu+j2wkLS3N/Hxz39q3b+969dVXXXv37nVFR0e7vvrqK/M9pa/bQw89ZPXlhqRp06a5qlSp4vr6669dX3zxhSslJcV13333Wf59RGhxQGhRs2fPdsXExLhKlSpl/sFs3LjR0usLVboEU1+bnDd9rXiN7OPLL7804TI2NtZ12223ufbs2WPO8xrZ07333muWPKvx48e7IiMjzbJafdPcvXu31ZcXsp588klXfHy8KzEx0dW7d2/XkSNHLP8+CtP/BGZMB+dr9+7dZvmfLjcrVaqU1ZeDXPAa2R+vkf3plMNPP/0kzZo1kxIlSlh9ObDR9xGhBQAAOAKFuAAAwBEILQAAwBEILQAAwBEILQAAwBEILQACTptRaXdNb+3cudO0FT969KhXjz9w4MDf9ok6cuRIga8TgL0QWgD4le60nDOg3H333fLyyy9nO3f69Gk5c+ZMni3Ev/76a3n//fe92tfmoosuMh1X3RYtWiRlypSRY8eOFfrvAcB6Ray+AADBbcCAAfLdd9+ZPZrcdCO2LVu2yLx587KNhgwcOFDatm1rdpPVzQ6z0t1l+/bta/atyUo33bvppps8x59++qkJOfv375fk5GSzA60GGQ1P2srfHZCeeuop0zoegHPQpwVAQHzwwQfagdt8/uCDD5rQ4N5XRneJbdeuneexd9xxh9mlWUNMzqZjOmLibjhWsWJFs/usBh2lz3/ZZZeZ0HP//fd7dg6ePn26vP766/LDDz+YcxpgdD8ivQ+AczA9BCAg7r33XtPldN++ffL888+b7e31cx11efjhh7M9tnHjxmZjNp0Suvnmm01Nixo5cqTcddddnsdp+KlSpYrn+O2335b169ebnWg1kDz77LNyxRVXyNNPPy2bN282n+tNp44ILIDz8F0LICB0NEV3jc25hb3WmWiAyeqRRx4x9S1asDt//nwTYKZNm2buq1y5crapJ7cdO3ZIv379pFy5cp5zaWlp0q1bN8+oi9LPdaoJgPMQWgAETKdOnaR06dLZzmnYmDVrVrZzGlZ0T5NHH31UXnvtNWnSpImpT9GAk5KSkutz63O0bt1aDh065DkXHh4ugwYNMs/htnfvXvPcAJyH0AIgYDSMaFFsVlqAm5OOvDzxxBPSq1cvM300evRozyZtqampuT53nz59JDMzU+68885s51944YW/jbQAcCZCC4CA+fe//53r9JAWxbrptJAWymr4qFevnqxevdpzn9a26IiLmz5OVwIVK1bMPEfx4sX/9mfqaiMtwnXbtWsXIy2AQxFaAPiN9mfREOJe7qxLnKtVq5btMbqix11cq6MuS5culZYtW5oaGF2urB566CETULRBnHulkHvkRoON9mHJyzPPPMNICxAkCC0A/EZX/9x6660mfGh9ia7cyUtcXJwJJjodlLMJnIafDh06SIUKFUxn3Lp162br+5IfRlqA4EFoAeA3N9xwg6f1vn4cMWKEmcLRAls1YcIE2bRpk5kK0mXOuVm7dq15vAaft956S1q0aGGea8iQIXLbbbf97fE6sqMjNhqA8htp0VGanKuWANgbzeUA+JX2Zpk6dapZsnzdddeZZcruzrS6kkfP6+qe8uXLmxERHQXREZrly5fLJ598Itu2bZPHHnvMFOZqbxX9kaVLp/v37y8XXnihjBs3TurXr+/58/TPaNq0qQk4Gkqy1stkHbnR5/r1118D+v8CwPkhtADwqzVr1sg333xj6la0m21udNXPpEmTpEuXLhIbG2s+//zzz83UknbKdXfAzSo9Pd0sZ9av0ekiAMGP0AIAAByBNv4AAMARCC0AAMARCC0AAMARCC0AAMARCC0AAMARCC0AAMARCC0AAMARCC0AAMARCC0AAECc4P8AEDvI21RrmtgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # 绘制散点图\n",
    "plt.scatter(y_test, y_pred)\n",
    "plt.plot([y_test.min(),y_test.max()], [y_test.min(),y_test.max()], 'k--')\n",
    "plt.xlabel('真实值')\n",
    "plt.ylabel('预测值')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78ad9d4c-a8a7-4b6f-8a1f-e501e8ca2bd5",
   "metadata": {},
   "source": [
    "## 建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e3c6632a-034c-4b38-8e2b-c4d6d4b3be18",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import cross_val_predict, train_test_split\n",
    "from sklearn import datasets\n",
    "from sklearn.datasets import fetch_california_housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a805b3d6-2a60-4e27-9cdf-d11441696089",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Income</th>\n",
       "      <th>Fasting insulin</th>\n",
       "      <th>Glycosylated serum protein</th>\n",
       "      <th>FBS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7.67</td>\n",
       "      <td>11.59</td>\n",
       "      <td>16.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.39</td>\n",
       "      <td>8.59</td>\n",
       "      <td>13.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.57</td>\n",
       "      <td>7.71</td>\n",
       "      <td>12.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.55</td>\n",
       "      <td>14.26</td>\n",
       "      <td>17.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>12.65</td>\n",
       "      <td>8.84</td>\n",
       "      <td>16.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6.93</td>\n",
       "      <td>11.93</td>\n",
       "      <td>15.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>16.33</td>\n",
       "      <td>8.80</td>\n",
       "      <td>11.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.80</td>\n",
       "      <td>7.22</td>\n",
       "      <td>12.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.09</td>\n",
       "      <td>9.40</td>\n",
       "      <td>15.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7.36</td>\n",
       "      <td>8.19</td>\n",
       "      <td>13.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8.33</td>\n",
       "      <td>10.11</td>\n",
       "      <td>13.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.92</td>\n",
       "      <td>6.92</td>\n",
       "      <td>11.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8.33</td>\n",
       "      <td>10.11</td>\n",
       "      <td>13.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.92</td>\n",
       "      <td>6.92</td>\n",
       "      <td>11.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10.48</td>\n",
       "      <td>10.95</td>\n",
       "      <td>13.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.68</td>\n",
       "      <td>8.32</td>\n",
       "      <td>11.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5.20</td>\n",
       "      <td>11.29</td>\n",
       "      <td>14.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>13.63</td>\n",
       "      <td>8.06</td>\n",
       "      <td>12.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.71</td>\n",
       "      <td>9.23</td>\n",
       "      <td>13.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5.96</td>\n",
       "      <td>10.47</td>\n",
       "      <td>14.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.55</td>\n",
       "      <td>8.19</td>\n",
       "      <td>14.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.02</td>\n",
       "      <td>12.01</td>\n",
       "      <td>14.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3.82</td>\n",
       "      <td>12.67</td>\n",
       "      <td>18.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3.98</td>\n",
       "      <td>10.45</td>\n",
       "      <td>17.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7.13</td>\n",
       "      <td>10.40</td>\n",
       "      <td>14.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5.10</td>\n",
       "      <td>9.10</td>\n",
       "      <td>14.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7.62</td>\n",
       "      <td>7.09</td>\n",
       "      <td>12.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10.48</td>\n",
       "      <td>10.95</td>\n",
       "      <td>13.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.68</td>\n",
       "      <td>8.32</td>\n",
       "      <td>11.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5.20</td>\n",
       "      <td>11.29</td>\n",
       "      <td>14.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>13.63</td>\n",
       "      <td>8.06</td>\n",
       "      <td>12.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.71</td>\n",
       "      <td>9.23</td>\n",
       "      <td>13.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5.96</td>\n",
       "      <td>10.47</td>\n",
       "      <td>14.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.55</td>\n",
       "      <td>8.19</td>\n",
       "      <td>14.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.02</td>\n",
       "      <td>12.01</td>\n",
       "      <td>14.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3.82</td>\n",
       "      <td>12.67</td>\n",
       "      <td>19.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3.98</td>\n",
       "      <td>10.45</td>\n",
       "      <td>19.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7.13</td>\n",
       "      <td>10.40</td>\n",
       "      <td>14.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5.10</td>\n",
       "      <td>9.10</td>\n",
       "      <td>14.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7.62</td>\n",
       "      <td>7.09</td>\n",
       "      <td>12.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7.67</td>\n",
       "      <td>11.59</td>\n",
       "      <td>16.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.39</td>\n",
       "      <td>8.59</td>\n",
       "      <td>13.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.57</td>\n",
       "      <td>7.71</td>\n",
       "      <td>12.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.55</td>\n",
       "      <td>14.26</td>\n",
       "      <td>17.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>12.65</td>\n",
       "      <td>8.84</td>\n",
       "      <td>16.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6.93</td>\n",
       "      <td>11.93</td>\n",
       "      <td>15.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>16.33</td>\n",
       "      <td>8.80</td>\n",
       "      <td>11.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.80</td>\n",
       "      <td>7.22</td>\n",
       "      <td>12.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.09</td>\n",
       "      <td>9.40</td>\n",
       "      <td>15.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7.36</td>\n",
       "      <td>8.19</td>\n",
       "      <td>13.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gender  Income  Fasting insulin  Glycosylated serum protein    FBS\n",
       "0        1       3             7.67                       11.59  16.03\n",
       "1        0       1             6.39                        8.59  13.41\n",
       "2        0       2             4.57                        7.71  12.64\n",
       "3        1       1             1.55                       14.26  17.02\n",
       "4        0       3            12.65                        8.84  16.97\n",
       "5        1       2             6.93                       11.93  15.50\n",
       "6        0       2            16.33                        8.80  11.09\n",
       "7        0       1             6.80                        7.22  12.07\n",
       "8        0       2             4.09                        9.40  15.35\n",
       "9        0       2             7.36                        8.19  13.42\n",
       "10       1       2             8.33                       10.11  13.74\n",
       "11       0       1             1.92                        6.92  11.56\n",
       "12       1       2             8.33                       10.11  13.74\n",
       "13       1       1             1.92                        6.92  11.56\n",
       "14       1       2            10.48                       10.95  13.05\n",
       "15       0       2             8.68                        8.32  11.94\n",
       "16       1       2             5.20                       11.29  14.67\n",
       "17       1       2            13.63                        8.06  12.20\n",
       "18       0       1             2.71                        9.23  13.17\n",
       "19       1       2             5.96                       10.47  14.22\n",
       "20       1       2             3.55                        8.19  14.53\n",
       "21       1       1             5.02                       12.01  14.62\n",
       "22       1       3             3.82                       12.67  18.23\n",
       "23       1       3             3.98                       10.45  17.36\n",
       "24       0       2             7.13                       10.40  14.63\n",
       "25       1       2             5.10                        9.10  14.31\n",
       "26       0       2             7.62                        7.09  12.30\n",
       "27       1       2            10.48                       10.95  13.05\n",
       "28       0       2             8.68                        8.32  11.94\n",
       "29       1       2             5.20                       11.29  14.67\n",
       "30       1       2            13.63                        8.06  12.20\n",
       "31       0       1             2.71                        9.23  13.17\n",
       "32       1       2             5.96                       10.47  14.22\n",
       "33       1       2             3.55                        8.19  14.53\n",
       "34       1       1             5.02                       12.01  14.62\n",
       "35       1       3             3.82                       12.67  19.44\n",
       "36       1       3             3.98                       10.45  19.72\n",
       "37       0       2             7.13                       10.40  14.63\n",
       "38       1       2             5.10                        9.10  14.31\n",
       "39       0       2             7.62                        7.09  12.30\n",
       "40       0       3             7.67                       11.59  16.03\n",
       "41       0       1             6.39                        8.59  13.41\n",
       "42       0       2             4.57                        7.71  12.64\n",
       "43       1       1             1.55                       14.26  17.02\n",
       "44       0       3            12.65                        8.84  16.97\n",
       "45       1       2             6.93                       11.93  15.50\n",
       "46       0       2            16.33                        8.80  11.09\n",
       "47       0       1             6.80                        7.22  12.07\n",
       "48       0       2             4.09                        9.40  15.35\n",
       "49       0       2             7.36                        8.19  13.42"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df = pd.read_csv('many.csv')\n",
    "data = df[['Gender','Income','Fasting insulin','Glycosylated serum protein']]\n",
    "target = df[['FBS']]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c706bddd-140d-4f29-9cd8-a6a4a049f25a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data,target, test_size=0.4, random_state=100)\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "y_pred = lr.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "66758d36-6139-44b1-90b7-b212765826c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 1.1767583876884462\n",
      "RMSE: 1.0847849499732407\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABLYAAAGqCAYAAAAWSs3SAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAyZtJREFUeJzs3Qd0FOXXBvAbeu+9o4D/j6aiKCiCBRUFC1bAhopdUVRUbNixK3ZFEVREsSAqikgREUFFlKogvffeS+Y7z9y82U2ym2yZ3ZnZfX7nxCzJZndMdqfc95YMy7IsISIiIiIiIiIi8plCbm8AERERERERERFRLBjYIiIiIiIiIiIiX2Jgi4iIiIiIiIiIfImBLSIiIiIiIiIi8iUGtoiIiIiIiIiIyJcY2CIiIiIiIiIiIl9iYIuIiIiIiIiIiHypiHhAZmamrF69WsqWLSsZGRlubw4REREREREREbnEsizZsWOH1KpVSwoVKuT9wBaCWnXr1nV7M4iIiIiIiIiIyCNWrFghderU8X5gC5laZoPLlSvn9uYQEREREREREZFLtm/fbidAmXiR5wNbpvwQQS0GtoiIiIiIiIiIKCOCdlVRN48fNWqUHHbYYVKkSBE56qij5J9//rG/PmfOHGndurVUrFhR+vbta9dDEhERERERERERJUpUga1FixbJ1VdfLU8//bSsWrVKmjRpIr169ZJ9+/bJOeecI8ccc4xMnz5d5s2bJ0OGDEnYRhMREREREREREWVYUaRWffvtt3aj9+uvv97+98SJE6Vz587y8ccfyzXXXCMrV66UUqVKycyZM+WWW26RX375JeTjIBCGj9y1k9u2bWMpIhERERERERFRGtu+fbuUL18+ojhRVD22unTpkuPf8+fPl8aNG9uBrDZt2thBLWjZsqWdtRXOgAED5NFHH43mqYmIiIiIiIiIonbo0CE5cOCA25tBuRQrVkwKFYq6Q1Z8GVvB9u/fL82aNZM777xTFi5cKHv37pXXX389+/tVq1aVBQsW2D23cmPGFhERERERERElEsIda9eula1bt7q9KRQCgloNGza0A1xJy9gK1r9/fyldurTdY+vBBx+U4sWL5/h+iRIlZPfu3SEDW7hv7vsTERERERERETnFBLWqVatmV5hFMmGPkiMzM9NudbVmzRqpV69eXH+bmAJbEyZMsLOzpk2bJkWLFpVKlSrZUxGD7dixI2TUjYiIiIiIiIgo0eWHJqhVuXJltzeHQkClH4JbBw8etGNLsYq6mHHJkiXSvXt3O7DVtGlT+2utW7eWqVOn5rgPSg0R8CIiIiIiIiIiSibTU8v0AifvMclQCELGI6rA1p49e+wG8uedd5507dpVdu7caX+cdNJJdv3j+++/b9/vqaeeko4dO0rhwoXj2jgiIiIiIiIiolix/DD1/zZRlSKOHTvWnnaIj0GDBuXI0Hr33XftTK6+ffvaDcB++uknRzaQiIiIiIiIiIgo7sAWMrXCDVFs0KCBLFq0SP78809p06YNa1iJiIiIiIiIiKK0a9cue1hfQTZu3CjLli2TY445psBG7UhActrmzZvtcsIyZcqIm2KeihhKjRo1pHPnzk4+JBERERERERFR2rjgggukTp06dm/z9957T7Zs2ZL9PUwQvPLKK+3bU6ZMkeeee05++eUXmTVrltSsWdNuyJ7bjTfeKPXr15fatWvLpk2b5K677sr+3rPPPiu9evWS1157zQ5UoQoPbaYefPBBuwXV9ddfL2eddVbI7ezTp4+0bdvWfvyHH35Yvvzyy5D3mzRpUkKTn5wP2RERERERERERUUw++OADWbhwoXz11VfyxhtvSPHixe1Eom3btslHH31kf2/w4MH214sU0Xyl4cOHS6dOnWT37t15Hg9ZVWim36FDBxkwYIB8++232d9DJhcCW3gc3A9tppDhhYbu48ePl8MOOyzsdpYsWVJKlChh316/fr0dDJszZ06OD/RlN03iE4WBLYre9u0iK1a4vRVEREREREREKad69eoyYcIE6datmxQtWlQuuugiO4h02mmn2YGo5cuXy7Bhw3L8zFNPPWUHob7//nvZu3evHZwyTPCpYcOG8vXXX0v79u3tfyNDCxlgxx13nPz33392/3RkdCHrC33Ty5cvL0cccYR9XwS69u/fn50p9uGHH9oBtt9++80OxOU32TDRDfwdLUWkNHHRRSKTJ4vMnCnSpInbW0NERERERESUP/QLD5HNlBSlSiG6E9FdEWx69NFH7cyqUvi5rKyqyy+/PLvUD//OHSzKyMiQTz/91O6LjsAYAmGFCxe2v7d9+3Y5ePCgDBkyxA54oX/6q6++ameAfffdd7Jy5Uo7O+uoo46SSpUq2cGqmTNnyo4dO+x+6uj5tWfPHrnnnnvskkP0V582bZqsW7cuu3cXHhdljE888YQdHFu7dq00a9bM/jqyxRKJgS2KzsGDIph4iRfmqFEiffu6vUVERERERERE+UNQy60m5zt3ikTQDB6QoYXA0SmnnCJjx461v4YAFT7KlSsX8mdGjhxpZ3ChFxeyp9BUPtg777xjB6JQvhgMGVx33323XHzxxXL22WfbQa2lS5fK559/LkcffbQ8//zzdpnim2++KXPnzrWDWoAeX/hAb61jjz3WDqrNmDHD7veFLLO///5bbr31Vrv3VzKwFJGis3ixBrXgxx/d3hoiIiIiIiKilFG2bFm7txYat5tpg8jCQsDLZGDl1rVrVzsYhYAYSgJ//vlnO9sLpYTI3EJW1YoQ7YTGjRsn5557rt1XC4E0NJc//fTT5ZZbbrHLEvF4sHr1aqlVq1aOn8U2bdiwwW5EP3v2bDuj67bbbrMzvDBUcPr06fZtNK1v06aNJBIztig6//4buI1yxL17UbDr5hYRERERERER5Q9lfciccuu5o4BG7t27d7eDVPhAAMlMFcyvlxWgUXu7du3sMkMEtdBMHj25HnroIcnt5JNPlt9//92+z1VXXSV//fWXHVh75JFH7LJD9NKCxYsX28Ez49dff7XLDqdOnSovvPCCPR3xkksusUshTz31VLtpPDK20KcLATD0/EokBrYoOv/8E7iNFydSCzt2dHOLiIiIiIiIiPKHnlQRlgN6wZ133iktWrSwM6HmzZuXnTFlGsPnF+AqVKhQdu8rBLoaNWpk99hasGCBNAnqk/3ZZ5/ZGVfowYUJi3g+9PhCP6/XX3/dzrhCHy0EuFBmaIwePdpuVI/Hqlixov01/Fy1atXybAvKFBFgSySWIlJsga2sNwnLEYmIiIiIiIicgwyrMWPG2P2tkBWF/lUnnniiHThC3y0EqcyEwtyQ3YWgWG74eZQeggmKISts2bJldgkiGsajvxYCVldccYUdkML3e/ToIRUqVMhRinj//ffbUxERDDP+/fdfu7dWlSpVpGfPnrJz5067/xZKIpHxlUgMbFFspYjnnqufGdgiIiIiIiIicsxjjz0mV199tdSpU8cuS3z//fftiYUoMUSZ3xlnnJFdJhgMmVydOnWyPyNghTJAlBgOHTrUDjbhMyYdogcWMsEA2WBVq1a1A1gXXHCB/TPHHXec/T00h58wYYK9LcFK58p8Q/8uBMcQAOvSpYvdX8t8IKsLWWOJxMAWRTce1WRs3Xqrfv7rL4SEXd0sIiIiIiIiolSAQBImG2JaIaBUEAEtBKieeeYZe+ogglMHDhyQNWvW2JlVJssLTd5vv/12qVu3rrRs2dIuKUSg65xzzrE/UFaIfljokWWyrY488kh7eiHui4wrZGzhvigtRGALfbieffZZOyMrN2SOISMLze7RhD4cBMsSiT22KHJr1ohs365liO3aibRsKTJrlsj48SLdurm9dURERERERES+dvTRR8tLL71k97dCGeKPP/5oB6IApYnz58+3m71/9NFH0rt3b3n88cft7yGY9fXXX8vZZ59tZ2NhsmGJXIPe3nvvPTuj6sorr7R7cG3cuFGGDRtmlxUis2ratGl2KeHAgQPtwBgeo1+/fjJgwAA5/vjjZdCgQXaTeECwCyWN7du3l/POO09Gjhxpb++oUaOkefPm2c+JyYr79u1L6O8sw0p06CwCaFSGMZTo2l+uXDm3N4fCmTBB5LTTRBo1EvnvPxFEkF94QeSaa/AOcXvriIiIiIiIiLKbrC9ZskQaNmyYJ8Djt/+PUNuPryNjqkyZMlHHXzD5EJle+HkEqDDx8P/+7/+y74MA1gknnCDNmjXL/tp3331nlygi8BUOHg/BruDeW7H+jaKJEzFjiyJnyhDNC/700zWwhT5biI9mpUASERERERERUfzCBeViDdaVCwoSoX8XGsHndt111+X5GjLBCoLHw0eysccWxR7YOukkzA5FpziRBQtc3TQiIiIiIiKKQ//+IuecI5LgsjEipzGwRZEzzeL+9z/9XKqU9toCTkckIiIiIiLyp/37RQYMEPn2W5HJk93eGqKoMLBFsWdsmXJEYGCLiIiIiIjIn+bOFTlwQG///bfbW0MUFQa2KDLbtomsXh0+sDVxYmBHSERERERERP7x11+hbxP5AANbFJn58/VzzZoi5csHvn700SKVK4vs2CHy+++ubR4RERERERHFKDiYxYwt8hkGtii6MkTTX8soVEjktNP0NssRiYiIiIiI/B3YQm/l3bvd3BoKYffu3XLw4MEcX8vMzLS/Drt27YrocVauXClzUXrqgM2bN8vOnTvFbcmfw0ip018ruBxxxAgNbD3ySNI3jYiIiIiIiGKUmRnI0ipcWOTQIZE5c0SOO87tLUtbNWrUkPLly0vJkiVl27ZtcvHFF8vff/8tq1atsgNJW7ZskcMOO0wOHToklStXlgkTJsjxxx8v5513njz++OPy9NNP20Evo0WLFvb34IsvvpA//vhDPvroI/vfzz77rPTq1Utee+01O1DVt29fef/99+XBBx+Url27yvXXXy9nnXVWyO3s06ePtG3bVm688UZ5+OGH5csvvwx5v0mTJtnbmSgMbJEzgS347TftxRVcqkhERERERETe9d9/SPcRKVlS5IQTRMaP1wwuBrZcs3btWvvzokWL7MDR1VdfLf+XdS0+aNAgmTx5snzwwQc5fmbkyJFy0UUXSefOne3A1osvvmh//ddff5Xly5dL7dq17c/FixeXIkUCoaBChQrZga1jjz1WihUrJu+++65kZGTYQbPx48fbjxUOAm8lSpSwb69fv94OhnXr1i3HfRo0aGA/biIxsEWRQTpqqFJEqF9fpHFj3SH+9JNIViSYiIiIiIiIfFKG2LKlyLHHamCLfbZchbLC+++/3w5EPfXUU9lBLVi3bp00atQoz880btxYZsyYIYULF7YDSci2QmCqffv2Mm3aNJk1a5ZMmTJFWrdunf0zyNCqV6+eXeI4f/58OxusZs2a9vP99NNPdtbYEUccYd8XgS584LHxOIsXL5aFCxfaQTAEx/C9cHCfRGKPLSrY/v0IFYfP2ArO2mKfLSIiIiIiIv8FtjAY7Kijcn4thViWJqa58YHnjgaCR8ja+vnnn+Xyyy+3v4YsLWQ/vf3223apYPPmze1yxM8++8wOMvXr1y/HY6CE8brrrsv+N4JPuQNMuM93330n48aNkyFDhtiBqn379slvv/1mP+6OHTvs56xataod5DLZW8gkmzp1qh1kM7dR+ogyRmwXgmN4LtzG1w8cOCCJxIwtKhgysRB9LVtWpFat8IGtN95gYIuIiIiIiMivgS18wKxZeg2InlspAj3Wy5Rx57nRX7106cjui8wnBIOGDRsmjzzyiFhZUTEEihBkQiYVemS1bNlSrr32Wvu+KAmcOHGi3UcLJYlQtGhROxiVn4YNG8rdd99t9/A6++yzpVKlSrJ06VL5/PPP5eijj5bnn3/eLlN888037Ybz6KMFV155pf2B3looYcS2IVvsueees0sR0Q/s1ltvlV9++UWSgRlbFF0ZYrgUwlNO0Z3eggUiy5cndfOIiIiIiIgoBgiamMBWq1YiKHErVUpkzx69tqOk++uvv+xm7ygHfOGFF+zSQZQXmumHCHShUbyZgli4cGE7Q+rHH3+UU0891Q5omQAZAl75QabWueeea/fVOuWUU+w+XKeffrrccsstsmTJEjsbC1avXi21ciW5YDs2bNggd911l8yePVv27Nkjt912mx18Q5+v6dOn27fr168vbdq0kURiYIviaxxvIBJsmgsya4uIiIiIiMj7Vq0S2bhRkxSaN9fPRx6p30uxPluI1yFzyo0PPHekkAG1YMEC+eabb+z+Vpg02LRpUylXrpz9fQS4cBvZVcHKli1rZ2wh4ISgFpq5o4QQwvW/Ovnkk+X333+XunXryltvvSUPPPCAncGFEsd77rnH7qUFKHVs0qRJ9s+hIX27du3s7DBklb388st2f65PP/3Uzvj64Ycf7GAWbuMD2WSJxMAWORPYAvbZIiIiIiIi8o8ZM/Rz06YiWdPtUrXPFoqPUA7oxkc8vdOrV68uw4cPzy5JRJZUcPZUZmZm9u1LL71Uhg4damdPzZs3L/t+e/fute+XO8CFPlooaUTgbMuWLXamGIJmCIqhaf2yZcvsPloIcJ2AiZlZRo8ebQe/rrrqKqlYsWJ2I/pq1aqF+L1nFJg5Fi8Gtii+iYihAluYohH05iIiIiIiIiKP99cyzO0Uy9jyK/TJQhN2NHUHZFgFT0ncu3ev/RnBJ0xRRL8sBKQQfELJIjKnUGqIyYf7MRguSPfu3e37ogQRDeORXYWA1RVXXGEHpPD9Hj16SIUKFXIE0zCx8cMPP5TixYtnf+3ff/+1e2tVqVJFevbsaWdwIfusVKlSdsZXIjGwRflDgMoEtgrK2Dr+eG0wj1RW7gSJiIiIiIj8F9gKztiKdpwfJcyRRx4pL730krz++ut2o3eU/aG5e9euXe3v9+/f3y4fLF26tGzfvl2+/fZbO9urQ4cOdo+s66+/3m5Inxsyu1CyiADWBRdcYGeGHZfVZgjN4SdMmCBXX311jp/BcwRbsWKFHRxDAKxLly52fy3zgRJGTHlMJAa2KH8rVuj4CDSgO+yw/O+L+5x8st5mOSIREREREZH/Alum1xYSFlavdm3T0hkCUwhGFSlSJEcPrffff9/OukIA6oMPPpDrrrvOLi/E1xFEQiAKmVlo/o5phpdffrn9+bLLLrPvh6wvlBYiGys4YIbphbgvMq6QsXXOOefYpYV4PPThevbZZ+2MrNzwXMjI+uqrr+wm9OGYMspEYWCL8mdevJiOkTVdIV/ss0VEREREROR9mzYFJtqbLC1APyTThibF+mz5xXPPPWeX811zzTX2v//44w9p1aqVrFmzRsaOHWv3tUKpIb7WoUMH6dixowwcOFDKlCljB5mQQfXkk0/aP/vQQw/ZGVjou/Xwww/L008/Laeddpr9vY0bN9o/h5LBadOm2R9ffPGFPRkRvbfw2Gj8fuutt9pljSNGjMjeRgS7ECxr3769fPTRR3ZWFwJno0aNsksnzcf8+fOzyygTJcNKdOgswmgk6ka3bduW3emfPOLll0X69BG54AKRL76ILBCGkkXU2m7ZojtFIiIiIiIi8pZx4zQx4fDDRRYuzPm9K64Q+egjkccfF3nwQfEj9J5asmSJNGzYUEqYxvg+hQASMrIQXMptxYoV9lTD3P/vof6f0feqaNGi2b2xkHGFABXKGYP7dg0aNMhuFt+sWbPsr3333Xd2gAw9tMLB42Fbg3tvxfo3iiZOxIwtcmYionHEESJ16oggIjt5ckI3jYiIiIiIiBwsQzRSdDKiXxUuXDhkUAvq5gpqQbhAHjK6goNOKHVEI/jgoBagxDE4qAXo65VfUMs8XqRBLScxsEXOTEQ0UKvLckQiIiIiIiL/BrZSaDKiB4rUKMF/Gwa2yNmMLWBgi4iIiIiIyP8ZW4sXi2zbJn6EkjvYjWFo5ElohG8y0uIRaLFPFKqZ4IYNgRLDSGU1opOZM0XWrROpXj0x20dERERERETR27VLZP788IGtSpVE6tXT5vK4rmvfXvwGwZIKFSrI+vXr7X9jel/wNEByV2ZmpmzYsMH+uwRPf4wFA1tUcBkianbLlIn856pV0wg/0lbHjxfp0SNhm0hERERERERRmjULdWAiNWuK1KgR+j64pkNgC5ldPgxsQY2s/zcT3CJvKVSokNSrVy/ugCMDW+RsGWJwOSICWyhHZGCLiIiIiIjIO2bMCJ+tZeB7X3/t6z5bCJjUrFlTqlWrJgcOHHB7cyiXYsWK2cGteDGwRYkJbHXsKPLccxrYwkoAUz6JiIiIiIi8318rBScjoiwx3j5O5F1sHk+JCWyddJIIxnyuWhUoaSQiIiIiIiJ/BLbM9+bNQ5fv5GwXUQwY2KLwTEDqf/+L/mdLlhRp105vczoiERERERGRN6Akb86cggNbaB5fsaLef+7cpG0eUbQY2KLQ9uwRWbo09owt02cLGNgiIiIiIiLyBpOBVb68SMOG4e+HdjKmHNHHfbYo9TGwRaFh9Ct6Y2HMa9Wq8QW2fvpJo/xERERERETknTLEgnohp1CfLUpdDGxRwWWIsTZ+x06wShWRnTtFpk1zdPOIiIiIiIgoQRMRDXMfZmyRhzGwRc43jjcwtvO00/Q2yxGJiIiIiIj80TjeCC5FzMxM7HYRxYiBLUpcYAvYZ4uIiIiIiMgbEJwy2VeRBLZQwYNp9zt2iCxZkvDNI4oFA1vk/ETEUIGt338X2bo1/u0iIiIiIiKi2CxapK1iSpSI7FqvaFGR5s31NvtskUcxsEV5HToksmCBMxlbGBHbpImuDKCJPBEREREREbnDBKdatBApUiSyn2GfLfI4BrYoL6SY7tunUfz69eN/PJYjEhEREREReSew1apV5D/DyYjkcQxsUfgyRGRaFS4c/+MxsEVEREREROSviYgGM7bI4xjYosQ1jjdOPlkDZP/9J7JsmTOPSURERERERJGzrOgmIhotW4pkZIisXi2yfn3CNo8oVgxsUeIDW+XLixx/vN5m1hYREREREVHyITC1YYMmHaDHVqTKlBFp3FhvM2uLPIiBLUrcRMRgLEckIiIiIiJyj8nWwnVeyZLR/Sz7bJGHMbBFedNTnc7YCg5sjR+vExKJiIiIiIgoeWIpQzTYZ4s8jIEtymndOpGtW7WGGs3jnXLccSJly4ps2sQoPxERERERkR8mIhrM2CIPY2CLQpchNmwoUqKEc49btKjIKafobZYjEhEREREReX8iomF+ZsECkV27nN0uojgxsEU5JaIM0WCfLSIiIiIiouTbvDkwod5kX0WjenWRGjW0dc2sWY5vHlE8GNii5Ae2fvlFZPdu5x+fiIiIiIiI8jK9sVCZU6FCbI/BPlvkUQxsUehSxEQEttCzq25dkf37RSZPdv7xiYiIiIiIyNnG8Qb7bJFHMbBFoTO2MALWaWhIz3JEIiIiIiIi/zSON5ixRR7FwBYF7NghsnJl4jK2gIEtIiIiIiIi/2ZszZ4tcvCgM9tF5AAGtihvGSIaA1asmJjnOO00/YyGg+vWJeY5iIiIiIiISKG/sbnWiyewdfjhImXKiOzdKzJ/vmObRxQvBrYowOzsElGGaFStGtiZjhuXuOchIiIiIiIiTSrIzNQEhpo1Y3+cQoVEjjxSb7PPFnkIA1uUnImIwViOSERERERE5J8yRIN9tsiDGNgidwNblpXY5yIiIiIiIkpnTga2OBmRPIiBLUpuKSK0aydSooTI6tWBYBoRERERERF5cyJiqIwtJimQRzCwRerAAZGFC5OTsYWg1kkn6W2WIxIRERERESXuOg89tpzK2GrWTKRIEZHNm0VWrIj/8YgcwMAWKQS1MLK1dGmROnUS/3zss0VERERERJRYqJDZv1+kXDmRhg3jf7zixUWaNtXb7LNFfg1sbdy4URo2bChLly7N/tq7774rdevWlVKlSsnJJ58sixcvdno7KZlliBkZyQts/fST7miJiIiIiIgoMWWI6I2FqYZOYJ8t8phC0Qa1unTpkiOotWjRInnsscdk1KhR8u+//8rhhx8uPXv2TMS2Uio0jjdathSpWlVk1y6RadOS85xERERERETpxMnG8QYnI5KfA1vdunWTHj165PjaX3/9JW3atJFWrVpJvXr15JprrpGFplcT+UeyA1tYLejYUW+zHJGIiIiIiMgfgS1mbJGfA1uDBg2S3r175/ha06ZNZcKECfL333/Ltm3b5I033pDTTZkZ+UeyJiIGY58tIiIiIiKixMjMDGRVOTERMXdga9kykS1bnHtcohgViebO6K2VGwJbF110kRydFQHGfX777bd8H2ffvn32h7F9+/ZoNoOchjGtJrCVrIyt4MDWH3/oDrFixeQ9NxERERERUSpD72tca6PhewQJDN98I7JggciddxbQdrlCBZEGDUTQogiBs1NOcXSziaIVd/e433//Xb755huZNm2abN26Vbp37y5nn322WAiWhDFgwAApX7589gcaz5OLVq4U2blTx7Y2apS858X0RexgsZIwcWLynpeIiIiIiCjVmVLBFi1EihbN966Y54WuQ3ffLVJAnopiny1KpcDW8OHD7d5bxx9/vB2keuKJJ+yG8jNnzgz7M/369bPLFs3HihUr4t0MiofJ1jr88AJ3eI5jOSIREREREZGr/bWmTtVcB3O7QOyzRakU2MrMzJT169dn/3vHjh2ye/duOXToUNifKV68uJQrVy7HB6VR4/hgDGwRERERERG5GtgaOzZwmxlblNI9tkI56aST5KqrrrKnIlavXl3effddqVGjhrRs2dKZLaTUDmydfLKWQC5aJLJkCZq0JX8biIiIiIiIUk0iA1smY2vePJG9e0VKlIh1K4ncz9i68MIL5b777pOXX35ZevbsaffZGjlypBRNdkkb+WsiolG2rEibNnqbWVtERERERETxW7NGZN06kUKFRApIOtm4UeTPPwP/Rk/4oKKs8P2SK1cWQaXW3LnObDNRMgNbaAzfAFMQBNMSMuShhx6SZcuWyf79+2XGjBnZExLJJ9zM2AKWIxIRERERETlnxoxA8kKpUvnedfx4XONrj3lzSfj77wU8PsYmss8WpUrGFvncli0ayXcrYws6dgzsUfPpzUZERERERESJKUM84wyR44/X2+yzRX7CwFa6M9laSCVFWaAbjjtOBAMEEGQzKwtERERERESU0MAWMrViDmwxY4s8goGtdOdmfy0DzeNPOUVvsxyRiIiIiIgoKYEtXA6uXClSvDgGwwUCWyhFzMws4DnMY8+cGcGdiRKHga1053Z/LYN9toiIiIiIiOK3datOnA/OqgrDXH61by9SsqRI8+Y64HDbNpH//ivgeZo00Tvv2qVT7olcwsBWuvNaYGvKFN0xEhERERERUfRMzysMfKtUKd+7BpchQtGiIsccE2E5IipvzMRFliOSixjYSndeKEWExo1F6tUTOXBA5Oef3d0WIiIiIiIivzJ9iwsoQ9y3T2TixJx5BhBTny02kCcXMbCVzvbuDaSoup2xhXGxLEckIiIiIiJKSn+tqVNFdu8WqV5dpEWLwNdjmozIjC1yEQNb6WzBAm3yV6GC7s3cxsAWERERERFRUgJbpgwRl2GFCuUNbKEn/J49BTwXM7bIAxjYSmfBZYjImHLbaafpdsyZI7JmjdtbQ0RERERE5C+IRJnrvAgDW6a/loEOMdWqiRw8GEG8Cj22EBVbu1Y/iFzAwFY680rjeKNKlcDOd9w4t7eGiIiIiIjIX2bPFjl0SKRqVZFatcLebcOGQCuujh1zfg+5BhGXI5YqpdMRgVlb5BIGttKZ1wJbwHJEIiIiIiKi+MoQW7XKtypn/HgRy9KEq5o1836ffbbITxjYSmdemYgYKrCFjC3saYmIiIiIiMjRiYgmjyB3GaLByYjkJwxspSukp86f772MrRNPFClRQntszZ3r9tYQERERERGlVON45A+E669ltG6tn5cs0bLFfDFji1zGwFa6WrZMZO9ekWLFRBo2FM9AUKt9e73NckQiIiIiIqLIoNs7emwVENhC4c7KlSLFi4u0axf6PuXLBwp7CszaMhlbCxeK7NgR06YTxYOBrXQvQ0Sjv8KFxVPYZ4uIiIiIiCj6azwkL5QtK3L44WHvZrK1kE9QsmT4hzPliL//XsDzolF97dqaCjZrVixbThQXBrbSlRcbx+cObE2aJLJvn9tbQ0RERERE5H2mFBAZVIXCX+oXVIZosM8W+QUDW+nKy4GtFi1EqlUT2b1bZNo0t7eGiIiIiIgoJRrHI2/gp5+iC2whYyszs4DnZp8tchEDW+nKixMRDawudOyot1mOSERERERE5Ejj+F9/1fyB6tU1nyA/+D5aIG/dKvLffwU8NzO2yEUMbKUj1D57OWML2GeLiIiIiIgo8ms8E1TKJ7AVXIaYkZH/QxYtKnLMMRGWI5rnRPP6Awci324iBzCwlY4wr3XzZt2THXGEeDqwNX26yJYtbm8NERERERGRdy1ZIrJtm069b9o07v5axnHHRdhAvkEDkXLlRPbvD1QHESUJA1vpyOxosPPJbwyGmzBVA9lkKOaeMMHtrSEiIiIiIvJ+GWLz5ppqFSa/wdzNdH4pSMQN5NFOxpQjss8WJRkDW+nIlCF6sb9WMJYjEhERERERFcwEk1q1CnuX8eO1YrFlS5EaNaILbM2cKbJ3bwF3Zp8tcgkDW+nI6/21DAa2iIiIiIiIHJmIGG0ZItSvrwPr0TarwEQsZmyRSxjYSkd+CWx16CBSpIjI4sX6QURERERERFFPRESmViyBLbRljrgc0Tw3MrbwhERJwsBWOvfY8nopYtmyIm3b6m1mbREREREREeW1dq1+IAqFOsMwuQ2rVomUKCHSrl10D28ayBcY2ELTevT32rpVZNmy6J6EKA4MbKWbnTtFli/3R8YWsByRiIiIiIio4GwtTLwvXTrkXUy2Vvv20c8PMxlbBU5GxETGZs30NvtsURIxsJVu5s/Xz1WrilSuLL4JbGEy4qFDbm8NERERERGRr8oQIZYyRKN1a/2M7jCYrJgv9tkiFzCwlW78UoZoHHusSPnyIlu2iPz5p9tbQ0RERERE5KuJiPv2ifz0U+yBrQoVApePBWZtBffZIkoSBrbSjV8axxtoHn/qqXqb5YhERERERERRTUT89VeRPXtEatQQad48tqeIuIE8M7bIBQxspRu/BbaAfbaIiIiIiIjy2rYtMEE+TGDLlCHisgr95WMRcQP5I4/UzytWiGzaFNuTEUWJga1047dSxODAFpYa0PyeiIiIiIiIAiV/9eqJVKrkeH+tUA3kLSufO6KNzGGH5dw2ogRjYCudHDwo8t9//svYOvxwkQYNRA4cEPn5Z7e3hoiIiIiIyBeN49Hs3VQqduwY+9O0bClSooTI1q2BS8qw2GeLkoyBrXSyaJEGh0qVEqlbV3wD+bIsR6RIvPCCyP33F7CMRERERESUHoGtceMCFYLosRWrokUDvenZZ4u8hoGtdCxDPOIIkUI++9MzsEUFWbtW5O67RQYMYGYfEREREaWHAiYiOlGGGHUDeWZsUZL5LLpBadc43sBkRGRuzZ0rsnq121tDXmSWo2DwYDe3hIiIiIgo8TDqcN68sBlbKGJwJbBlMraQWIFtJEowBrbSiZ8DW5UrixxzTN4ABpERnM33+eci27e7uTVERERERIk1Z47IoUMiVaqI1K6d59uIeSEnAL2x2rWL/+nMZMSZM0X27s3njrVqiVStqtuGbSRKMAa20okfJyIGYzkihYPlKPO6KFlSZPdukREj3N4qIiIiIqLk9NdCdUsu5vS4QwcNbsUL87wQr0Lb5nyrDLEt7LNFScTAVjpd+Ps5Yys4sIWMLTYHp9zLUWvWaFCrXz/92vvvu71VRERERESuNY43ZYjmMipeiFexzxZ5EQNb6QI5qDt2iBQuLNK4sfjSCSfoREc0CWdKK4U6ardvL9Krl77Of/01kKVIRERERJRGga19+0R++sm5/lox99lixhYlAQNb6cJc4B9+uEixYuJLxYtr4AJYjkjBzOsBy1E1a4qcdZb+e8gQVzeLiIiIiCghDh4UmTUr7ETEKVO0b3uNGiLNmzv3tFFnbGEb0WuLKIEY2EoXpgzRr/21DPbZolDLUZMm5Xx9XHONfh46VA/6RERERESpZP58jVyVKSPSqFGebwdPQwzRfitmrVvr58WLRTZsyOeOqBJCtQ163/73n3MbQBQCA1vpwu/9tQwTuEAgAwENoqlT9YBZvbpIixb6tc6ddToMylZ/+MHtLSQiIiIicpYp8TvySJFChfINbDmpQgWRI47Q23/8kc8d0RqkZUu9zT5blGAMbKWLVAlsIY8WAQysTqCHEpHJ3uvYMbAchXLbK67Q24MHu7dtRERERERJ7q+1fn3g2zhFdhr7bJHXMLCVbj22/F6KiMCF2TuzHJGCXwe5l6Ouvlo/f/11AXnSRERERESpE9jCEHkTV0JOgNM4GZG8hoGtdLBtm8iaNakR2AL22SJj82aR6dNDL0ehLPHYY7XH1rBhrmweEREREZHjLCvfwFa4dV+nA1u//66bElHGVr53JIoPA1vpVIZYq5ZI+fLieyaA8eefIps2ub015KYJE/Qg2ayZvr5zM03kUY7IgykRERERpYKlS0W2bhUpWlTPg4PglNf01zL5AE5D6ywMrN+ypYC+8FhoRv8vVE+YRAuiBGBgKx2kShmiUbu2SNOmutdGYIPSl1mOCnfU7tZNj7qzZ4vMmJHUTSMiIiIiSgiTrYX+w+gtG2TePJHVq0VKlBBp1y4xT494WqtWgaytsEqWDFyDss8WJRADW+kgVRrHB2M5IkWyHFWxosgFF+htNpEnIiIiolSQTxmiOT3u0EGDW4nCPlvkJQxspYNUD2yxxCw9LVqkadhYMsKROxzTRP7jj0X27k3a5hERERERuRXYSlR/LYOTEclLGNhKB6lWiggIZCCggcAGAhyUfky23gkniJQuHf5+p54qUq+e9iH46qukbR4RERERUTIDW1jDnTQpuYEtJGLlu3bMjC1KAga2Ut2+fYHATyplbJUpI9K2rd5mOWJ6Kqi/llG4sMhVV+nt999P/HYREfkEEp5HjSqg8S8REXnLunXaRCsjQ+TII3N8a8oUkT17RGrWzNNT3nENGohUrSpy4EABMSuTsYVr0m3bErtRlLYY2Ep1OFvNzBQpV073cA547DGR9u01WcpV7LOVvg4eDAwOiGTcS8+egdfK8uWJ3TYiIp/45BOR88/XD1b1ExH5LFurSRNd7A9Thoi4VyLh8Y87LoIG8pUri9Stq7dnzUrsRlHaYmArncoQHdi7Icj+5JMikyeLnHaayKpV4h4T0ECAA4EOSh/Tp+uLEc3hjzmm4PsfdpjIySfrldsHHyRjC4mIPA0r7A8/HJighQ8iIvJ3fy2z3p/oMkSDfbbIKxjYSnUON47/9luR/fv19uLFIh07iqxfL+449liRChU0wIFAB6UPc9RGdBWlhpG45ppAOSKyGImI0hhi/AsXBv795Zdubg0REcUb2MI1mfkWrtGSgZMRySsY2Ep1Dge2Pv88UNmFjFIkhCFxavNmST4ENNAYHFiOmF4i7a8V7MILRcqW1YgsUg6JiNK4/SbaCkDr1vqZgS0iIn8HtsaNCyRHVauWnE0xpYhon7VxYz53ZMYWJRgDW6nOwYmIO3aIfP+93u7TR2T8eJEaNbRU+swzXeoFyD5b6QcvxKlTow9slSol0q2b3h48ODHbRkTkA++8o+0Ga9XSgBbWibCIjrg/ERF52PbtgXTbXIGt4P5ayYLimSOOiKDPltnWuXMD5T9EDmJgK5Wh3MoEthzI2PruO13lbdxYpEUL/YyVAfQDRCVg584iu3ZJcpnABgIdCHhQ6vvpJ+2pdvjhIg0bRvezV18dSD3EiQERUZrBcRq9MuGhh0Tq1BHp0EH/PXKkq5tGREQFmTlTP6N0pkqV7C+jjawbga3grK18yxHr19coGBo8sqkjJQADW6kMy7GY91q0qDbPdqgM8aKLAn3oMUYWyVLly+t42fPOE9m7V5LHBDcQ6Jg0KYlPTL4qQzTatNHsxd27RUaMcHzTiIi87vXXdVI8Dp2m9eAFF+hnliMSEXncjBkhs7WQCLVmjUjJkiInnpjcTTJ9tvLN2MLFoylHZJ8tSgAGtlKZydZCalWRInGv8CJjywS2gmG/OmaMTptFeSK+n9QMU5Yjppd4Als4qJqsLTSRJyJKI2gZ8MwzevuRR0SKFdPb55+vn3/9VS+MiIjIX/21TLYWMnBLlHAvsIXMsbDYZ4sSiIGtVOZg43gErpDkghXeEJNl7UQYTEzEjnT0aJEePTSJKikY2EofK1dqwLZQocDggGhdcYU2lMEVnAn+EhGlgZde0mEvSFy97LLA12vX1uM4fPWVa5tHRERxBraSXYYILVuKFC+ux5fgabt5cDIiJRADW6nMwcBWqDLE3LBCgBNirAB/8YUmxqDNV8IhwIGNwv8vAh+UukzwEmO8UKcfi5o1Rc46S28za4uI0sSmTSIvvqi3MRER8f1gLEckIvI4NDs2/amCAltoA/Pzz+4FtnDt16pVBH22gksRk3KRSOmEga1U5lDjeLTpQjZWqDLE3DAdEa2LcML80UciN95YQEqqEypVEjn22Jxzbim1A1vxHrVNY5kPPkhiaiERkXuefVZnrOC64sIL836/a1f9PHGirroTEZHHzJmj56249kHz+Czoc4zrNUy6bdrUnU0z5Yj5BrZwTYooGAY4LV2arE2jNMHAVjpkbKHmIA5Ibd25U/efSJQpCBrIDxum1WKDBonccUcSglssR0x9WNkxgctY+msFwwjPqlVF1q7VOlsiohSGvlmvvqq3n3hCj8+5NWqk5SSHDol8803SN5GIiCItQ0R6VFAJjSlDxOlxuMqaZE1GzLeBPAaaNW+ut9lnixzGwFaq2rhRP+CII+J6KJQVFlSGmNull4q8957efuUVkQcekMQygQ4EPhIeRSNXzJolsmGDTikwzWBihdWiyy/X2yxHJKIU99RTupqPXefZZ4e/H8sRiYj8NxHRzf5auTO2UGWIismw2GeLEoSBrVTP1qpfX6R06ZgfBjumr7+OrAwxt549Rd54Q28PGCDy5JOSOG3bipQqJbJ+vcjs2Ql8InKNOWqffLKu+MTLlCPiBY6AGRFRClq2TOTtt/U2jsP5LVCZwNYPP2imNhERebtx/Lp1gRhRx44ubZfogLEqVUT27y8gZsXJiJQgDGylen+tOMsQx4/X8eCo2Y4lSeamm0Sef15vP/hgoHGt4zCKA93rgeWIqcn8XeMtQzSQCo3ebOhVgNpZIqIU9PjjIgcO6JyVgobJYreIkkQsan3/fbK2kIiICoQ6cVQv5ApsmS4d+FK1ai5tm+iiSUR9tpixRQnCwFaqcmgiopmGiFXcUD05InHXXTqBydx+6y1JDPbZSl2ooZk82dnAVnDW1uDBLGElopTz338iQ4YEemtFcmHCckQiIg9asEBk926tUGnc2FNliEZEgS00c8TBZtUqVkyQoxjYSlUOBLawwvvVV7GVIeaGbK377gtkcQ0dKs4zAQ/Mu8XcW0odv/yiKQS1a8edhZhDt26a7YfyVdO3gIgoRfTvr4v8mJeBiv1ImMAWpiHzUEpE5BGmdO/II3X8vOiarFMDw5PWQL5sWU0NBmZtkYMY2EpVDpQiYuT3li2a1tquXXybg8A8mtf27h1IlBkxQpzVrJlIzZqa3fPrrw4/OHmmDDGfBjGYHhzVmPqKFQNXccjaIiJKEYjXf/JJ5NlaBqYfYw0BPbbQjoCIiDw2ETHL3Lk69bZkSZETTxTPBLYWLhTZtCmfO7LPFiUAA1upCGmq6BYbZ8ZWcBli1sJAXBCPeOklkV69RDIzRS67LNCY3hF4AtM1keWIqSWC5ShchGERq0kTkdWro3jsq6/Wzx9/zPQEIkoZDz+sq/kXXxy4hogE2g507aq3WY5IROTdiYjBc5VQgOA2rBfjPLzArC322SIvBLY2btwoDRs2lKVLl+b53r333ivnnHOOU9tGsZo/X89mK1cWqVo1podAP+2RI50pQ8x9woweWwhq4Tlwwm12yo5gn63Ug0mX5sB32mlh7zZwoAh2S1gheuCBKB4f3ZTr1RPZujVQe0tE5GN//KG7MxxzH300+p83iayjRumxmoiIXITruhATEc01lJPtZ5PSZ4sZW+R2YAtBrS5duoQMas2aNUveeOMNGYirS/J9GSLaVG3cqLExM2zQKcj+QjPbCy/UkbDnn6/P5wiTsYVVjXxzYMk3TC0MDoJhxr3gT/3ss4F/o4dbxC2z8IK86iq9/f778W4tEZHr0NcSLr88tsTtk07S4z/2rWZuBxERuWT5cu0PU6SItl4RLTKYNMk7/bWMqCYjIhlj166kbBelvqgCW926dZMePXrk+XpmZqZcf/310qdPHznssMOc3D5yqXG8KUNEOQL2oU7DY6Ly6+yztSUWGtvmuwOMFHpsYV45VjbYHCT1+muF8cwz2l8Lg1bQDx5//jvvjGLQYc+egefCyQMRkU9hoQir+DjOonl8vtBZ/rnndEBHEPzseefpbZYjEhG5zGQ2IaiVVXOI3TaCW7VqiTRtKp4LbKEUMex5eI0aItWr6x3QEJIo2YGtQYMGSW/T/TvIW2+9JbNnz5YGDRrI119/LfuRhkO+DWzhPNecyDpZhphbsWIaQEMlGPojderkUKk1yxFTBw54BeRZY1rwq6/qbQwoQJCrRAldxYq4shABeTQowPN98IFDG09ElFzYhZlsLfSzLHCt8aOPRO65R6R79zxXIKYcEW0J0BeTiIhckk8ZIrK18pmrlHRYZEbsDcOcFi3K547ss0VuBrbQWyu3nTt3Sv/+/e1MrWXLlslLL70k7dq1kz1Iwwlj3759sn379hwf5J1SxClTRNat0waACDolEqZ4oIcHJnmgxRFiF/PmORjYijhlhzz7WkbkCkfIMKM5H39cV6zwGkIGINpl3XWXfq9vX+xvInwujOo05Yi8iiMiH8KFDkoHscuMqNcgml7CypV56rfR0hBT2bELRs8uIiJyidk/B01EDA5seQkSF0zMin22yFdTEb/88kvZtWuXTJw4UR599FH58ccfZceOHfLhhx+G/ZkBAwZI+fLlsz/q1q0b72aQgS6vCxbElbFlyhBRhlC0qCRcmTIio0eLHHus9vXCyfR//8XxgO3b614VkyExb5b8y2TdoeELoqC54HXy7rt6e8CAwIrVffdpljNWil57LcLnQtM3XMUtXsymMkTk62ytm28WqVOngB+YOVNk2rTAv3ONKUbmK9oEAMsRiYi8k7G1dq3uwoPbC3tJVH22mLFFXglsrVy5Utq0aSNVqlSx/12kSBFp2bKlLMwnoNCvXz/Ztm1b9seKFSvi3QwylizRjuw4I61fP+ofR6LKF18kvgwxt/LlRcaMEWnRQnfWCG4hLhWT0qVFTjhBb7McMaX7a2GcPUpnkamF2FdwsPTJJwMZXRs2RPBcpUppgy4YPDjuTSciSiaUXk+frodABPcL9Pbb+hnp2YD06VxMOSICW0yAJiJyAU5ikTqL1dsjj7S/NG5cIIGralXxnKgmI86axfG75I3AVp06dfKUHaIksXbt2mF/pnjx4lKuXLkcH+RwGeIRR+ic7yhh8Xb1ahH8SZK9AoAJTIhjYNMR60QZJLYlJuyz5X8HDoj89FPYwBYWeD75RG+bIFYwDDrEMXPbNpFHHonwOa++Wj9/9pl2oyci8gEE+B96SG/fcUfYAbIBaGyJ/lqmHBHnC1j+z7WidNZZWtaItco5cxK08UREVHC2VqNGWlkQdHnjtTLE3IEtnKuHbQmC/x+sxKCfiKk2InIzsNW5c2eZN2+e3UAe2VuvvPKKzJw5Uy4wy3zkq8bxJlvr3HOzh24kFQZkYJghGt6iIgyZW+vXx/BAJhAyYQJXAfwKUVZcfGEpKmuFKtj99+tn9Dw2iz7BChcWeemlQGJCRL3b2rTR3nQI1o8YEe//ARFRUnz6qcjcuZr9bHoM5mv4cJEdO0QaN9b0bNPDMFc5IrJfzzxTb7MckYjI/TLECOYquQ5tuVHMhSIiUzKZBxZUzPk9+2yRFwJblStXlu+++06GDh0qTZo0kYEDB8qIESPYN8uHgS3sKE1/rWSWIeaGZD8Et9AfBAloWI3AZI2oIDcX5RXIumHXW38yy1FIHcyVfYhx9t9/ryPpH3ss/ENg0OH552s2w913R/CcSPM2TeRZjkhEPklu7d8/MDDDVBZG1DT+hht0/4rVrAjKEYmIyN3AFrJn0bYFrWcxOMmLcDp93HF6m322yNOBLcuypEGDBtn/PvHEE2Xq1Kmye/duWbRokZxzzjlObiPFUooYQ2ALvTmWL9cVWrdTW/HyQnALGVyI9HfqFGVlGNJ1kO4FLEdMqf5aCMD266e3r71WM5nz8+yzOgQBgbAffojgea+4Ql8/U6cG3k9ERB71wQdaKojk1ttvj/BgjwlbGLKCmm0zLQYmTdIRxUFwSoddItqgcB4LEZG7ExFNthYWb92orklIny1mbJEXMrbIQ3DFbzK2UE4VJZOthSlIIQbQJV2TJtocEb23kHTVpYvIrl1RPAD7bPkXLqx+/z1kYAsTNH/9VecjoHl8QVBpc+utevvOOyOoTMU4RXSjh/ffj2nziYiSAb1LTNYqAv5YmIo4W+vii7VWBLBC0LSp7iCxChCkUiWRU07R2yNHOrr5RESUH5SMm1HxWdlNJrDldhKC45MROaGE4sTAVipBXio6ZaOsAFEhH5Yh5ta8ue7A0Tdk8mQtK0OPwYiYgAh6NeHAQP6B3mgY0YkAbdDMenzJ9Nbq3VukVq3IHg5NlREgRZ+tQYOiaCKPVAj2aCMij3rnHc20xr7wxhsj+AGcI6C/lilDDGbKEXP12QKWIxIRucA0qEKflqpV7RawaMfhh8BW69b6GZm+mzaFuVOzZpoSjDusXJnMzaMUxMBWKjHZWui8HmVuKgLlaNaOTC1MQfISZN5iARmDM5DBhcAbmhFG1LkQvwsEJsx0PfJ1GSKux2bP1kDnvfdG/nDoOWMmIyLLK1elTV5IW0RdD4LFY8ZEu/VERAmHDGYzERbB+4gyrTEJcfduzc4yDeMNU4743Xd5DrJYVELPFKwTYeo8ERElv7/WL7/oAj/iXDHOCUsaZPuiagJMEUYeKL/A8QjYZ4vixMBWKjH9gOIoQ0QFFgJIXtO2rci33+r+D6Vol10WYSINyxFTJrCF6yxTenjPPXrAjAaSE/DW2LhR5KmnCrgzes9cfrneZjkiEXnQ66+LrFunazhm5kWBqdnBTeMRqQqGTr9obImGliYlIEvNmnochq++cur/gIiIoglsBZch5t6Fe7kcMWxgC9hnixzCwFYqiXEiIs51P/vMe2WIuaFJIk6oEXNAIA4n8ihNyxcDW/6zZInIokU68hB/9CzvvadZhbjuiqhBci5oIP/CC3p74EB9rHyZK0WU5WzYEP0TEhElCCoKn3lGbyMbFcfFAmEgBsZpIbULQzJyQxsDM/yH0xGJiNxngj1ZjePN5YzXyxBj7rNFFAcGtlJJjIEtnOeiLyGqF1GB5WVnnikyYoSWY3/4ochNNxXQa/DUU/VkHdlsrN32B3PUbtNGpGzZ7JIb0yAZJTexZhWizBYnA8j+QtZXgQ3ejj1WUwOHDYvtCYmIEuCll0Q2b9YsVGQwR8Rka3XrpvXZoQT32cp1cO3aNTA4EZmvRESU4OkguEiDo4+2u2OYlltm8LufMrbCXq8xY4scwsBWKomxFNGUIXbqlB1H8DS0AUGbEKTgonFunz757Cxx8o7gBDBryx9CLEe9+qq2u0LJzXXXxf7QeM0gawuxzi++yFNtEz5ra/BgTmshIk9Aj90XX9TbCPhjoadAiIJhVShU0/hgHTtqRhc60psrqCxoWYnrj0OHRL75Jp7/AyIiKtDcubq4imuZevXsPsMmeQttYP3gyCM1cQLHLRRj5BvYWro0gia4ROExsJUq0BPDdHSNMmPLi9MQC4IFZ5SmmbKyBx/M584sR/QPXDGNH5/j77ZlS6Dk5tFHIyy5KSARywTH7ryzgHJWvNBwREbH+hkz4ntiIiIHPPusDvrFtcCFF0b4Q0OH6uo/fgi9tMJBUMssKnA6IhGRN/prZWTk6K/lFzhnN5WGYcsREbirX19vsxyR4sDAVqpla9WoIVKhQsQ/Nm+efqD/kGmt4RdXX63NcwHNwM10qLCBLSx1FNiUi1yF4BEiWRh7mJVph4s4LOAgINWjhzNPgyyHcuVE/vxTS1rDwsHWXMkha4uIyEVr1mgGKzzxhGafFgjZpm+/Hb5pfG5mOmI+fbZwgYXgGhERJT6whd24HwNbYNZS8m0gzz5b5AAGttK8DBHlWGYniViC39x8s8hzz+ltZG2h70geGOWEpkxoAD5rVrI3kaJhjtrojVakiH0Rh4w8QOAyopKbCFSrJvLAA3r7/vu1h1e+EVT4+GOdsUxE5BIs4uzZoy0IMcU4ImiKNX++SJkykTXk6tJFg19YaFixIse3MJW9SRPtU/jdd7H9PxARUXSBLRQOYApuqVIiJ5wgvhJRA3n22SIHMLCV5o3j/ViGmNvdd2uJmiktMwvTOfJgO3TQ2yxH9Dbz98nKsnv8cb2IQ2zS6YxCTFZEz67VqwPB0ZAQZKtXT9PGOOeeiFyybFng+IZAf8Sj3k3TeKS8RtJIE81bzJVTrmZaeE6WIxIRJaE1h+lz2KpV9rovhoWjQ4YfA1uIWaEiPiRmbJEDGNhK48AWJiEigalIkcAgJL/CpLx779XbmJT4wQe57sA+W963c6fIr7/q7dNPt5tMDhqk/xwwIIqLuAjhxABljoDPYYdmIk3sqqv09vvvO7sRREQRQqD/wAGNteMjIuvXByJQN94Y+ZNFUI44ejSTWImIEgIXaSgnQIpWkya+LUM0g0eqVNFM31wzSfJmbKE/TtjoF1H+GNhK41JEU4aIkbGVKomvIeiB4Mdtt2k7EVSPffZZiMDW5Mk8E/cqjCjEVVuDBiKHHy4PP6zDYM48M5Bw5zQ0Xm7XTrPCUJIYVs+egcAopoWR8/DG5XuTKOw1zpAhgd5aEUMwHvtVNDkxK+KRMKtdEyfqcJogaH9Yp45ec3GtiBKKF7iUrkxJXsuWsmd/Yfvyxa+BLVyjmT5bYcsR69bVi1Gc+GMaJFEMGNhKBQiBL1wYdcZWKpQh5t5xvvyyyLXXao94VF1kV1GgMUitWnrh/MsvLm8pFVSGOGt2hgwfHugpk8jXzIsv6m00kf/jj3yWm5D/jeBLnnRAipuJRqOjf//+emJDRNnwtkBlSufOWpodERwI33kn0DQ+GkccoR8Iio0Zk+NbLEekpMDrDgNczj9fM7qJ0rS/Fi5bcPmCBYUoWyn7p4E8Dizss0VxYmArFSCohTNeNIatXTuiH1myRCfCocoK5wypAhOi0IMEQS1cGyNoZ8dLsMPs2FHvxCVmzwe20NgdsY5LLrFbCyRU69YiV1yht/v00ecN6ZprAhkQnK7pLKRXDh2qF9EYWYk0OhOsJ0pzaBr8yScxZGthEvDixToZ5tJLo39ik7X19dd5vmUCW/gW3rZEjkIaNfpK4DPKYdu314aYRGkY2DJliCg+cboth6cayLPPFsWJga1UK0OMcI9nyhBR4oW651SCYB2ukbt21WQ2tApBlRv7bHkYTliRepyRIVPKnCnffqt/R/SUSQZkhZUsKTJlSiCTMWTdIhov40LRfkGRIzCt9JZbAhfSuAjHmQ9W7gYPzifSSJQeUJaNt8HFFwcWtCNiOs0jco/JwNEyfbbQTCtX9AqxZ/SY37yZu0NKAEx0WbpUpGZNfaHhIh+jQBHlJUp12OGHCGz5sQwxd8YWyupx3AiJGVsUJwa20rRxfKqVIeaGhvgoZTvrLF3wQ/nGb5XOCuwwcTFN3oHMAhzLjzlW+g0ol50ghbHyyYD07nvu0dsYQhCy1RMaeHbrprfZRN45vXuLbNwo0ry5Zm6hsyhW59HAB3XFuJrftMntrSRyBcqjMYwV2chm+m/EiwWm8Xu0ZYgGAgkIKmAibK4Sfiw8mLgXyxHJ8fGfaJoKL70kMm2alsWuWKER1azzBaKUhWlGOO8pUkTWVG5uD/oKLjzxI7TPaty4gHJEk7GF80BWRlAMGNhKw8AWel8jIQI7SWQ1pSpMvUNm2imnaHuGTpdVlr8bZUXyxo93e/MoWFYW3ZjDb7EbZOJvhyyFZOrbV9uwoUz3lVcKKEdEACZXQ2WKAa7YUWOFq2QEC4sVE6lfX2TCBL2wQYQab+KWLXkxQ2npwQf18+WXR7V2JfLee9qi4MQTNWgcC7wvu3QpcDoi3sa8BiHH3H23ri6hpAD9CNDjEhOTseCB4y5WLJHNS5SqZszQz02byrhfStg30ZbD7xU2BZYjIoBdooRetGE0OlGUGNhKpVLECM96zerqSSeJ1KghKQ3lZegBcsIJuuh8+qr3ZZ78H8sRvZZy/eOPkikZ0m/6hfaXMN0SWVTJhEods0iMPjbr14c5KqPkF2mAI0YkdwNTDXLRb7wxEFXEqLXgC+r77gus1CP7BKXEd93FKVmUNlDihxIUxHfRPD5iCGgNGqS3zXssVsF9tnKVBZ96qs57wNsz7Ao8UTSw6IiSAqQoYoXJtNdAugfeDKaBKrJ5H3qIpeqUmlKsDDHiwBYOdi1a6G322aIYMLDld1gmDe6xFYFUL0PMDT31v/tO5JhjRDbuKSMdZZws/G4BT4i8Aj0z1q2TEcWukJmLytgXSohpuAFZEXid7NgRJmMMJ9kma4srxvG54w77727vt8JdteOPgSkX5uIcIyzRqIGjoCnF4fBksrV69dKklYh9/72WbSEYEO+BHgFlrKAjlXXOnBzfQmatSehiOSLFDX3cbr9db998s2bq5n7BffRR4I2BFSj0j+NiB6VoYMs66ujsdfhUCGwFT0YMewlm+mwxsEUxYGArFeqw0YsGUe7DDy/w7qtWaYPs4DKCdIB+1D/8INK8aaaskVpy2tqPZNnExW5vFsGPP8oBKSIPFhmQXYVQubI7m4JFYrT0ACQ8hOxTixNpZBRNnRoIKlN00Iz6ww81UIgAIS6c80ule/NNzRhBvx80m0DAC6v5DE5TisIqvSnLxpTYqJim8T175v/eigTef2bwSj7liAhs8e1IcXnjDV20QL0VpuOGgmMGpsq8+64eh4cNEznzTJEtW5K9tUQJD2zNLt/OXv/DbrhtW/G9I4/UjhNoH4Y5TPn22WIDeYoBA1t+Zy6s0ZGvaNEC7z5ypH5GaV7t2pJWECwZN6GQNCm5QpZLfTnt0sqcHu0FP/4og+UaWbS7lh236NPH3c1BiS4GICIZEpVveS7WUL979tl6m03ko4ea4Ouv19v4YwedrWGKaVjnnKNBLfRXwQo9VvZxe82axG8zkUvZWkhciaosG000kaIM5n0Wr+ByxFw6ddLYGdqhcGAdxQy1/yZzF2OKK1bM/5iAUkS8zjGpeNIkPalFViGR32GYDjJuscCxqpn9+eSTdZHD7/D/YOJWYcsRmbFFcWBgK1Uax7MMMSLVq4uM7z1KGspiWbSxgj1hhAMSXbR3r+ye9Ic8Jlr3h4s5lI667dlndVUJKeCo6snj6qv18wcfaL8PihxS8hBRbtRIV96zWgJ1764L9WPG5POzCCoi2+u11/RqGmmYKFcJkUlC5Fdoxj59uq7SR12WjUwWROUxNQX96ZyAoDIyZTCiMddqELYRwS1gOSLF7P77RbZt0w7ZWeX+mNGC+NZVV+kxIg/UZqEEAZFfLPJiimfYq2UinzCZSo0aydifNePWJM2mggL7bOGcDscbLFoiXY0oCgxspdFEROwf0IwWkJGSrupc1EbGy2lSO2OV/evDAYNZ7C759Vd5be+1slpqS/36VsxT6Z2Gfjam1QeyttD6I4fOnbUsbu3aAiIxlKe+CtPaACWIpUrZ2Slot4XhiOht1q2byH//5fMYOOG55RbtvYW8dqxunn++ZqegLJvIx3ABj57YgPdFtWpR/DB2VAhsOdE0PveKkLka+eabfMsRiaKGgKnpWYlFi8KF7Wt7BLR279b1IyRohZy8iUbTuEJGlgeyvpDaYkoTiHwc2NrT8rjsa7ZU6K8VcWALqyVNmuhtZm1RlBjYSqPAFo71uIhE87569SR9HX20NKy0XSZYp0j1Svtl5kxdccYUaUqurd9MlqdFUxIefTTDU6nW6GuD2BUWgk3LmmxI50KneWA5YmQQtbruOr19661a8ykiAwfqtYypqMai/Xnn6d3z1bSpnhkhAwzBLjRFQ447LpKIfOrTT7XNEPpCIqgeFQSdsMqNaBiCvU7CmxJCZEeigTzafKIUMd+gNFFuiFbheICT0yuvtEvTEZ/CyxfDh7F2gVZaQ4fqekbIPm61aumqLVoE7N2rK7dolsmmb+TjwNbk8ufYXReQkBhhUY6vGsjjfzNsqTH7bFGMGNjyuygmIqZ7GWI2nCWddpo0kf9k3KXv2r23MKEDJ+dYHaTkee6TurJFKknT2luz40RegQtL078WrT/yZPWZ6YjoO8N61oLdc4/2/2nYUGTAgOxg+513Bso/cW2CaxTE63GNE3KFPhgioc89JzJunDYNxFU1eq08+WSY2hUi70LClWkz1LdvdpuhyJkIPPZNCL47yfTZGj9eZOfOHN/Cdp56qt5msgxFBelYOAFDr6ynn7bfAxdfrIcKLHRMnKh3wdrFW2+F6XsJ+HkEXZGpiDvgwIK0ax4HyG+ygjljtx2fna2F13+qwJwzXHchqIXEgpDYZ4tixMCWn23erKnXEQS2UK3z0096O53LELNlFaw3nznMbtNTrpxOoMIqIRb8KPHWzt0kL6+91L795OOWHW/0ml69RJo107daVjuogObNRY49VntsYTIThYerE1yVAEqlypSxr2Uuu0yvQXAtgsQrtNDChTGuydFnCNPcI4KrajSWxxUR/h5o1oaSlKVLE/l/ReQoZKUsXKiZoqYUOmLo3o5SX1wBmcxIJyErHH3xcDWCg2YuLEekqCE999579fbDD4vUrGmX32KBw8SpEDTt0UMTcgGJWLhrSEgbxGRFrJLAq6+KdO3KEnXyDywaLFhg3xz7T52UK0MEHKJM1laBDeSZsUVRYmArFcoQ69YtsOM2ThCwcIW+nOgflPZMJ8bffpNjGm2zG4SjrBvNwnFtnO90NnLEk3dtlt1SWo4vOUvO6xltakJy4Dz5hRf0Nsrl8pTZmKwt9Adh2UNouKhAhBDQRO3UU+3hVehHjVITDDbE9YdZkcQJj4mBIXslxCC20CpV0jquIUN0f/jLL1rHwqAj+QBKTkyGaL9+MQzRMFf+uApKxEEeb9B8piOiUhF3wYXKypXOPz2loEcf1cVZDDno3dt+CSMuhdcRdtvBHTbQY+uVV/Q2Fjyykn7zwg8j3XHECM3oRXluhw7aD5PI67BAZ1myptqRMvufovbL+bTTJOUU2GfLBLZw0p0rQ5goPwxs+RnLEGPXoIGuPiPa99NPdvUSzn8waO3bb7VpaYFlUBQzBDbe/rGhffupzlM8nWZ95pkafEGJBKrpckCnc5w8o7nMjBkubaEPpl0tXqwB+GeftUs60QoF1zM4d0EsCgHE3EMn0XYFUKJqdnUFwgsJb17kt7dtq43z8ABY8t+61fH/NSKnvPOOTnhHKW7Ufd+xEmOabzvZND5cny0cJHNNg0W25Ykn6m1kWxLla948XdGAgQNlyh/F7B5agOxoLHzkdtttdrVi9mEF/RnDwgrlhAk6aheDRjAxEc3riLws6zxyXM0r7M/HHKMv4bQLbGFgSc2aumCMYB9RhBjYSoPG8biQRAsaYBliiKytrF8OpqOjDKpoUZ3QZiZTkfP697fkQGYROV3GyqnXaoDLy5C1hVJJXLChqi4b6iRMDY65sKQAZE2Zi5dBg2R/iXL2rwuBKjRExfUxSk5CefFFkfbttYk8rqdRtRIxZKygngUZAfjDDR+u2VuTJjnyv0XkdFIj2sIBjjslS0b5ADhwoc8fomJoFpkoWAFCZiRqs6dMyfNtliNSRHCxilpbBEfPO09WNjvTPjfF4hEWXxG0CgeVi6YUEWWLJlEx7Ot16lRt1rVsmUZeEewi8np/rUOnpWQZotG6dSAhC4eTfBvIs88WRYGBrTQIbKFqAOcPmIpsJqhSUGAL9YdZMB3RnCg99RQH3iXCnDkiH32kt58q0l+jFx6Ht5hJhEBP2hz9aJFeBB9/zAZtwTCJAaWauIi5+mqxzjjTrkhErz8Es0aP1n7v4SDA/NlnmuiFlhPoxxVVFiXSwHAFhOAaupWiGzGi16jzYq0xeQjKnNet07kKpro5KqZ2F2+w3OmPTsJjm8BZiHJEtDMCxI/R15MobCAWC4rFi8uep16yXzd4/bdsqedcBWVwP/KI9mQ01e3mfCIkZOYjuIWgFlZHcJKHbvREXvTXX5IpGfLjyv/luExJNWgej7cmhB1kzT5bFAMGttKgFJFliGHgIrdQIZH587UGJAsqmdB7Gq6/ngt8TnvgAcQ6MuQi+UyOPamkSKlS4gc4mcakRCweoclzjsbl9eppqRtrcALQIAvLccgiefFFO3nqww81gQoBK1zEFKRaNb0GQokwAmFmYlxUUIKCEyMTZEMtC8oUI65vJEocXGs/80xgHxP1MEO8jhEtxrHM9LJLJFOOiMadufoKosIffTwRgI64Nx6l34JH1ihc6+6+cv2AhjJ9ul7o4vAZSW85BL7QH/7mm/UliHM2c54bEh4cgbRLL9W0MPwADkjsi0leggW3OXNktrSQdVtL2H1/caqSqgosR2TGFsWAgS2/QtdlNCoqIGMLJ80YlAQMbOVSoUJgNEdQ1hagiS/aJyHTDSnyJjmO4oOFU1zwFJJD8rg85KvlKPQ5MOWpCM6hRM6GSE3Pnnqb5YiBMxXUEsLbb8vQURXs6wh4803tWxYp9JgwWZRoGvzFFzFsD1LE3ntPfxilVOhjgStwZLrw4oZchClvaBeA9SlkJcbUnAs6d9b0xkRDbQz6CmIKY4gDI8sRKV/PPadlgXXryktlHrKzrXAIRa93ZCxGCsEtVLnj0ItAavfuuvgRFlZHkFV9332BKDJ+mNm75KW+cwcOyNgSuniAwc7Y1aaqiBvIo4ctAtJEEWBgy69Qm4MLMvT4QVpDGOhhg+M2Yl9NmyZ1C31bjmhOmpASjxYNSMTBNQOaXVPs8HJFFRj0LPqx/E/m+66BABqao6oNA5ZMloXNBLawKoySt3SGckyUZ+Jq4/LLZXzJLtmJJPj7X3dd9A+J/u9Zi/z2YjvKWWOCq240Iu3YURcHbrpJM1D45iYXbNoUiP9iMQUX+FHBaxhTQBPdND4YUmrMmC5kbYUJbOGQitkNRNmWLs3u/j72yo+k7wPFsoO7SHyOFpIU331XE7HMIqTpJxv2BzBO8e239c2GkkSUJnKwCHmpv1ZJnT7rs9PjuAJbIdcX0SsVi5IYGYzKGqIIMLCVCmWI+TQkYBliFA3kczXwwQIfUuOxb0Vy3Pnns4VSPJA5iN4rxYtlSv8DD2h5gEk19gmsnmHB2TSUz45hYakZpa04OueoU0xDuEJHJkf16jLvplftiw1cdODiAxlXsUIgEdfTaLSN9yKyXGKCxl4//KARBdR9YRwqGhB+913sG0cU42samZ9YmI5psAsO8HgjoBQ6mjTIeJ2rF16h6g2xiIbTEiyo8S1FOdx1l30StfD4y+TS10+yT7mCJ+DGAvEplLhjfQLXv/iMtor5Qo8JrPoiSItpMOi/haAbkZtmzJDdUlImbz8yLQJbmOeDUzAs8GBwdshANO4E7LNFEWJgK4Ubx+OE+fvv9TYDW/n038HJDTrdzpyZ59tVq2p6O6oWUUZn0t4pOvidmUlHN7f6TerJCo1S4MDlMwiqdOigQU6TgZajiTwyKNL1RYKx6mh+IiJrn3xPzu5RwS6HxnUDfi3x/LnRt/rTT7WPD6qgUHqSo4l/NLAhffpo19LmzTVjC2mZuMJCFgxRgq1Zo03jAQHfmN4bpmk8LtSjTveKwznnBJbakb6aC8sRKQ8sHn75pewoVF7O2/iubN2aYZ9+oTS9oGbxBcGgERwbENtFC6+zz86nIbWBTC1EwLDQgRIwbAyafRG55a+/ZLKcJPsOFbWryo84QlIaFopNteHvv4e5E/tsUZT8d1VJEQe2sFqKFSxMOkZCAoU5I0Ihe4hyRAOrz2hgbU6eTJ8lii6xAG2NEEPsdygrbcdH/bWC4SQcpRP4jJYd2f0BkHKBtGksPf38s6QdpGgguHfokOy64Ao5563OdisV7H+Q+YgMyHiZBsMlS2rSFXqdxQUd7HEFhNHz8Prr2tSLJ1GUYJi6ixgqrqdxIR419B359VeN+MY0SjEOGAiBee3IUEXmS5jAFs5BGCcmuz9O7972tLcrGvws8xaVkJo1teWhUz2E8DgIpGLRCYu6CHKh6jxfyAaZNk2PAxjLiB9GBi9RsmExdOZM+VFOz87Wijfgm1J9tpixRRFiYCuFJyIGlyGmww7S6T5bwRD7Mg2scUGC/lsU+TmtmTJ59617peqMH3wd2DKLSKatFhJ/7P4AmO6IiQOQji8QvDFmz5ZDlatJj92Dsidd4eIWjfedgmsR8+tFKReCzXFBxO3ll0XGjBGpUUMXDTBUAjWn6Zp5RwmFgC/a/MCTT8Z4fDYPgNorRAmSLXg6Yi6Yy4DqSJQN53NYpXSB1MR//pHHSj4toxa3tMuPsFiI+KiTcAhGXArBYlToopVigcNv69QRmTw5kO6FlGyTSkmULAsXiuzcKWMzzkyLMsSYJiNy0A9FwvKAbdu24dVqf6YIHDxoWcWL4y1uWQsXhrzLzp2WVaqU3uXPP5O+hf4yb57+ovA73b0737s+8IDetUgRyxo/Pmlb6GvvvKO/sypVLGv78G/1H40bW363apVllS6t/zvDh2d9cepU/ULJktixWWnj77/1TSFi9e40P/vtNGVK4p7y3nsDv2o8vSM2bLCs88/XB8bHqada1ooVDj04kbrmmsDLKyY4wJcrpw/y44+WK2bN0ucvUUK3J5c77tBvX3WVK1tHXrF2rf1a/UK6Zu9WhwxJ7FNu2WJZRx+tz1WrVtjT5Jz277esXr0C+/4+ffRcmygZPvnEWi017JdeRoZlbdxopYX//gtcfu3bF+IOe/dmn1taS5e6sIXktzgRM7b8CE0uUWOI3Gs0nAkByQdYfEJPa5/1504+ZL2hzwJ+pwV0HUVfbCTlmAk8Ba4GpjmUoTz6qN5G2VjZX773fbaWgdXme+/V2/hsl9xg+QmvJ/wD88vTJSUPJYgHD8rAFu/KK2Oa2F9GQ19MFU0UZLpgkR2/aiyyo01e3JBahnoWpGdi+X/CBC1T+ewzBx6cSAcam/kSMQ9T+OQTHTmIEa2xjJNzAnrT4QQDzQZDpGWZckT0l+ek9jTWr5/M3l5Priz0kf1PVH1jsm0ioScqhtU0ayayerW28yxwWDF6Tbzzjk5NBPQbuPhiPZEmSrS//souQ0Q3BGS7pwMcwipV0suvEG2O9ToXb2RgiwiKAANbfmSiKegsGKZhLHoXAMsQI4BfUATliIAGvyiDwgU7JkSjN8qGDcnZTD964w2RVavEboRpT6M3v98UCGyZIU+oZMBJM86D7deS6XczeLCkBTSL/+svGVW6h/SZc012iSCuCRIJu77hw/XECLF+M/I9bvgb9uqlJ1HoI4Salksu0dpTBBOI4vDIIzr0ALMK2raN8UFM0/gbbnBvAAfeJ2Y6YohyRBwjq1XTtw+m4VIa+u032fT+KDlPRsmuzFJ2DPb555Pz1FijQL969HhE6S+CWxjYUOBr+r779MBi6iUx7RjDRYgSacYMGStnpFUZonnLofNDvg3k2WeLosDAlp8bx4fpr4UFVNP/ktMQIxRhYMu05EED68MOE1myRFuN4HdOOWEaHtoumYu5EuuXa7oCIhI4WUwBSOp5+mm9jYVee0DYFVfo/yPGaKZ6St/cuXYa4x9yrHQ/MBRJ9Pa1dt++yXn6ihX1mrp0aU2uuuceBx8cV0RTpmiqIYIHSLPBCRYadhPF2O8dyVZxZWth8iga2OHC2zT6c4vps4UG8rlGlGIXiExK4HTENJSZKQdvvUMulU9liRxmJ/chiRmzDpIFLRPHjxepX19bGKHnVkQLkUjLR1QMqSS42kbTrlQ/lpN7LEsyZ/ydo3F8OomqzxZRARjYSsGJiEjB3rlTs2SQcEARwBmP2XFGsDpXtarI6NGa8o74Ba4v2Gc6pxdeENm8WeOvV14ZFDTEUax8eUkV3bvrihPec/bETJxNmzFnqdxEHulR11wjS/fXlC7Fxsqe/UXsCerou5vMLFFkqX/wgd5G1hxKIB2D8hREIJBygqsjRLJPOkmkf3+H0sMonWD/gGYhyGY0i9AxN41HLTwORG5q106jy6gDxoEwTDkiEl94fEwzQ4ZI3+mXyHjpKKVLZdoLEG6UV+E8GIseaB0wb54GDZBFWCDs57GIYVYwkYKYjtOOKfFWrZJZm2rJeqkupUtbsWfy+hQnI5KTGNhKwcAWpyHGADUTGLcGWOKLAAI2WInGCiQmsz38cGI30U8QG3zxRb2NuIC9SptiZYgGknnsMkQRee+9rD4B6DkFiLikagDkpZdk6+/z5exCY2T9/or22yfZK/LBF9Bm8ub112tSi+MX8PjDXn65XqGj2R6+hjQAogj88YdmF2J/YfoORg2lsB9/rLeRGuk2BH5NEB/NtHJBYi7WMJDJOm1a8jePXLJ1qwy94y95WfrY/xz6QSFp0cK9zUFsCqd1OM3D2uVZZ4ns2BHBD6LdB164Zswizl3M+4/IKX/9lV2GeMopGXYybjoxpYgo6AgZdDaBLfT8wGo5UT4Y2PIbLPealOgQpYhowGfOL1mGGGPWVhTzyXHijj7Tppn1kCEJ2jafwe8Co96PPTZr1R7BAKT2p2BgC7CYix5PeHveeaeIdXZnzabAFR0mOaSa+fNl/4OPyQXypfyT+T979gKqkcqWdW+TECzo0kXLgrt2TUBbFFyhIx0M/VdwG8uLOOFCLzWOoaYCmMArYqNh1qQKNmyY7lhx7G/fXjzBlCOG6LOFC7RzztHbLEdMH7/f9L7csOM5+/ZD9x+ykwvdhrcMTu2QYIhdN16XEfWFx3EcKV/4n9i/X+Syy/QEh/t8SkDj+HQrQwRkcjZqlE+fLZxvoZYZWI5IBWBgy29wtYaQNlKxmuj0sWBYlUJvI6RdY5GJYuyzFcVJC8oQ0YYHrrtOZOJESWto5G16G6PvlJ01iBTiTZs08mGWZ1IMem1hgAvOgb/5oZhewaZiOeKhQ2JdfY1ct/81mSinSpkyll2Wiyb6bkImzEcf6SL7ihVa7pWQaWzovzJrlkiHDhpkuPZaXUXA65soBFQwoUUAshlRxRoTHJNMGSImcXglHRujSZG5heX2+fPDliMisMVYQOpb89N86frJJbJPSsi5bdfLI4+HHnDkBgy4xfuwXDmtLscCCBaDC1SypKYjm+aRiFJjwAjHfZIDdv8xVybLSWkb2IICG8izzxZFiIEtv5YhInqNg22YMkScTLo1LMm30FMBS8wrV4Y8Qc8PKpPMVDb87tO5zygaxWNhExOQTBJcdhYcUtxwEZSCGjQQ6aOVF3L33SL7r7hW/4EUylQanfnaa/LY1I7ygVwlhQtb8tlnGdlVvG7Dwh4GOyB+imACsucSol49XUVANBOvZ1y146rJZCUSZUEwxyx84FoYZVExQZoJymExvcRuWugRiBJgZx8mawtxL5yqoE1RyHHulDL27bXkgvMOyWqpLU3LLpcPx1Tz3Hkossi/+04HvyDIhYG3EcWn8D+CCcCvv663kamL0aZYSSaKw+TfitmB4LrV9obKV0gL7LNFTvHYIYcKlE8ZIg7OuKgDliHGAGc66JsTZTki4DwHZYho+rh1q57vpFIsI1JozmqadyNbK1uK9tfKrV8/7ePx338ib/7cTM+iEe1ECVEqWLRIPug7Wx4RbRL0xhsZdsN4L8Gu0fy60cge1x8JgbFv996rPViQJrZ6tb6+EU3jmFTKgovnX37RbE4T4IqJydbCCgrqqbzk3HPD9tnCYRU9jYDliKkdwL357KUybXtTqSBbZNSoDDvm6UUnnqgvVbwn8RmDjHMN9Qzv5pv1hzCKF+c1WBBFijBRLDZtkrEbNGhzxpmFPJOI62ZgK2RmLzO2KEIMbKVQ43iUwKFKERfWJj5DUTKBF1yNRAkL6Viwxor84sU66jzdrm+RoY92Wkjxz644RCMLXNmlQWALJ/Jolm96Pm2+9Cb9Ryr0YcrMlIkXvCq9Drxh//Peeyy7UbsXoX8KsijhppvyWQV0QqtWIjNm6BMBJgngxT9nTgKflPwAb3nTWwvXwzGX6+LA/skn3mkaHy6whSlyIZrbBZcjUmp6/aX9MnhiQykkh+STHt9Io1Pqipeddpq+HpFwi+E/yKaMeHInVi6RElyzpsjs2XpVzkwSisXff2c3jj+jS5p1jc+VkIWCGQzYRXZvyDuYa+A9e5K9eeQjDGylUGAruAwRyQQUA5ywwPffxzTxDH1G0W+oQgU9x8dwvHQZc47gAca6I3vNBHdskydrbSLmbqdBnvU112hVGq5FH/2vhy4L4+TX8VF9yTWv/6fSddYjckCKySVn75SnBnh7aRHZMQiw4qWHfeKaNQl8MqSlvPGGyDff6E4Af29k6w0cmD47AMoDGdTTp2tyx333xfFAmK6KVRLsWLzYPBMROwR4EcnDATDEYRX9xebOjbrKn3wAi6p33K0nnc9UeFrOHOSPkgEM9MQsEJwvI+P+1lujWH/C6x3Zus2b68EFmVuocSSKwuqfFsgcaSEZkmkHW9MVTpNN7CrkQiQmFFWpoqmVOJAQhcHAVoqUIqLaCUEFYBliHDCTGmc72HmalI8o4U/zxRd6Io9F9pibBfvM/ffrZ7R/ado0TBliGuRZ4yT5hRf09huDS8j8jrf4von82j9WyNlPniDbpIKc0HC1DP2ijOd6p+SG7Rs6VF+LqBLEUKuIGgXHA2MZEdTCPgRPdscdejuhUTXyIhxCHnpIb+NlgEzquJvGI1vLq/vQfKYjYqHHXLSZ8xRKnWExF194SA5ZheUy+UjueucIDfT7BI4LOE7gbfXmm9ofPuLgFnotIhsdzUQxSASpwngQogj9OE7358fWXmtPB0xn+fbZwhuUfbYoAh6/NKEcdu4M1PLnythCVjRSOLFjxLAuigNqyACNemLsAo9euu+8o7eRvYTVwFSGntmYBohUYjSPz8EEttJo3AvOcxHjQMD57k1ZqRoff+zL2tTduyw5t+MuWWbVl0YlVsioaTXssls/QBN5ZM2gqfzUqSK9eyfhSatXF/n2W20yjF/UDz9owNw0QKS0gPImLCzjtXfXXXE8EDJekamNtC8zadWLTDkiyvhRfp4LyxFTD2I5iGdu2lJYjpHpMqjDMMm46ELxm8suC5yvYVEqzzlMfvAGR6aWSc9HzTGiY8zUpQiMnVfb/nxGu7z7zHTDyYjkhCKOPAolhwmyYOm3UqWQZYgovUGmEMUBJUQ4W8PKM4JcyFWPAc5zUM341FNi9yLC1LyTT5aUg9VNNE0HtBmqXz/om2vXisyapastaZZn/fzzImPGiHw7raqMq9ZdOq4frsGNbt3ET1knl7VbJn9s/59Ulo3y3deHpEo1f62HNG6sb2GUQ+HiBRUkCW9ThNc7LnAwBRRXTVhhxM4ZT47obyrC/9fjj4u0by/pDoNcTKYurnHj6vVusrV69NAmfl6F0ajY+S9bphNDkb0SBIfUG28U+eMPkeXLNdmF/H3cxzkODu/VZJ2MLHSRlHx9tHczCguAHluIx95+uybrY5JnxOXDaNT13nvaYBVpmjj4I5UNJcQhppcTQeaOXTJuu0ZzzrjUYwNBXMzYQstStJDIc6rEjC2KhOUB27ZtQ+Kv/Zny8eGHOJewrPbtc3z54EHLql5dvzVmjGtbl1r+/lt/oRkZljV7dswPc+iQZV16qT5UhQqW9c8/Vsr5/HP9/ytd2rLWrQvzmm3VykpHvXvr/36Lamusg1LIsk4/3fKTO67dbm9/cdlj/XLLx5afDRigf4uiRS3rl1+S+MT79lnWPffovkSvB1P3o1o1y1q71kp3gwbpr6NqVcvasSOOB9qwwbKKFdMHmz7d8rzbbtNtvfbakN8+6ST99sCBSd8yctiTT2btT2WfNVlOtKzbb7dSwdNPB3ZnMb1Occ6DgwweoG1by1q/PgFbSangryF/2y+TMhk77NOEdJeZaVmVKulb548/Qtxh3rzAxQYufCltbIsiTsTcnhRoHD9lisi6dboqjBI4cmj1Gc3KkAqHpXc0zYqxzw9aK2GFGqVQyBpBv1H0l04FKLUzI+zvvDNEH5ng/lppCC+dDz8Umb2+hgyWa+S6ce/5Jl3hlYGWvPxeWfv20EZPyIkDs0p0feree3Ul8LPPtK8KevmjH2nCYdnxmWdEevYU+e8/SVkY/4f+YlddpaU5Xm/CliBorWbaMyKTtUyZOB4MNexYukYW8THHiOehHPHVV3WIAkqxcr0GUI6IykqUIyalLJgSAn9eM+3zdblF2lVdIPLIt5IKcJxA5hbew8jeQrswZHNFDOXCGJSDsdg46WvbVveHaTA4h6Iz9qtd9udTqs6RYsU8OBQkyZDsiXJEVDqgzxYOezngPYQMSNRAoxzmiCNc2lLyNMsDmLEVoa5dNVr98sshs0J69nRty1LTnDmBLIsZM+J6KCzaNWyoD3XCCZa1Z4+VEt57T/+fKle2rK1bQyy/1Kypdxg3zkpXeLvaySxFN1nbpKxlPfaY5XWjRuGln2lv99OF+1nW3LlWKti507JatNC/x3HHpc770BPwGilRQn+5zz9vpatXXtFfQa1alrV7dxwPhHTfRo30wZAC5gf791tW+fK6zb/+mufbS5fqtwoVCpHdS76ApImyZfXveFPRrNREnAikEJy63HVXIGn/o49i/EU1aKAPgjSUyZMTsKXkZ6fVmmu/PF45/Wu3N8Uz+vfXt8wVV4S5w/HH6x0++STJW0Z+iROl55JqCk1ExKKoSSbiNESHNWsW6IcU52hDZGhhCjr6jP76a6DPqJ+hD7ppsorMBPy/5TBvnk6DQwPtE0+UdIVWS1hoWn+gkgyQfpqF4eE//vTpIt27ZeKUXq6Xt+Wex8rmGnPpX+i/jTZnaFGIBqXoCRfxBCzKH14jL78c2CEgJS7NYCH5ySf1NlrtxNVeB9M4sCqNvlp+6cuHXkNnnaW3v/46z7fRgguJZ9j9hfg2edzWrdorbccOkfbV/5WXD9ws0rq1ZqOmWObIc88Fjg9IQo06aR+VFUjPRwrK5s3aYxQTJYgwlGe3yOQ1jezbZ3Qu6vbm+KeBPPtsUQEY2PJTN1pTxhJUioh0zVWr9NwXk9jIYQhooZwCuffoehsH/NlQgoHm/p98EneszHWYao0hnXXqaPAmD1OGiGbSfhmjl6BrPfSShZekjyxdfEjHmHoQ+j536WLJ7j2F5EwZI6+3GiwZ9/SVVIL+vri+wNsaMUYMLySHYEoG6s1wvEIwBlfAaeS117QtQMOGItdcE+eDmabxV1wRZz1jkiHyARi+EgKnI/oTBol0766noXWr75PP1rWXYnJAS09TsOwYwS28nxGzM//vqCiMekLuxIlaloiSYuwTUZbO1ZS09/OEg7LfKib1ZJk06cIy1dyBrfnzRbZsCXEHTkakAqTe0ShVLVqkDY2QcoD6/VzTENHaonhx9zYvZaGG24xYf/jhuB8OPdDMWOknnhAZOlR8aft2nfYICNCFzExI8/5awbp00b/9Pikh98oz2njNg6vxZ5+NC/MMaSkzZUSRy6TIkHdTcswqFgGwIg933CHy009ub1EKXQ0OGqTHKGQb3XabpItt2/SaFZDJGtfwS0yTRWohJHyEp8M6ddJ9BnqChugpZwJb48bp74z8Ab000fumZElLvqp0rVSTDZp6bkaZpSDE6959V+TSSzVWj9cuBn5GBU26cKKOAw1g1CLGg+J8ntLW2E81anNG0Z8k47CGbm+OZ1SpInL44Xo7ZC5BcMYWA8QUAgNbfixDzBqnjPe0CWyhGTIlCGpKChfWszrUEcYJ54Ko1IHrrvPnRfWLL4ps3KgldiGrELA6OWmS3j7jDEl3eMvid5aRYckIuVSmfLJCo4MegT8X9iGoHq2VsUZGS2cp99DtIi1aSKrq00fksst0Nf7ii7WnPzkAdZ7DhulVISL3uJ0GXnpJV5hxiMbrKi6DB+uF7wkn+O89WKGCyMkn6+0Q9Yb4/SB7GYEClOeT9w0fHgjaDr58orT6Z5iWCQwYIKkOp34YAINERAyGwCIyBjZF/SDYQQwcqCcDWN0855y0y2ilgLETdcHwjMaLs6/pSJlYOSqS8sDxEOcW69frAhBRLgxs+W0iYlB/LfTCwcUYkrjOPNO9TUt5jRoFojcOZG2ZbK1LLgmsAiLt1i82bBB54YXA/0fIhB5MA0LDGaTi++3CLIGDNq/NKk+6c/8AyfxkhHgBAuRICkFLnzJF9sho6yyp07KyriynMHN9gcx2BGm7dhXZs8ftrUoRJ52kCwKARjWLF0sq27RJA9eAaWq4jo0ZIq0mrddv2VoGrv6B5Yi+h0mypqz23tv3SreRlwbSEnF8T5N2AihfxxodeiOhjRzOv6OGUaAjR2qKOxZKsZ9cuTIBW0xehvYxc1dVlAzJlNPaM3MvqsAWMiDNNET22aIQUq/GJNUDW0H9tUy2Fsqc4mpSSwXDbOsPPtA8dGQidegQ18OZ/j4ITKK/aOfO+hlpuF6HRdqdO0VatconU3Ds2EDNF1ejsj3+RIZ88uF++X3/8TL8ue/lsuvd3iINTuK1WLhQpow4eIEcVXiOyPu/x1lL5Q84R8J1BsZK4wIOLaLwNudL1qF9JvaXv/yiDWrwGVeIKQjZLEi+QJVE3NnT2Hei2V3FippK6NfAFi7ikdqCqHGuAxsCW2iy//33GijA+5C8B0kRaA+FQTEI5jx56D79e+I89NZbJZ2g1QeOFSjXxykgglzItm/ZMsoHQuoXHgAZWzNn6pCiatUkZeEXhqEiPKhmQxk2tJY/pFLbrCANhQxsYeE1z0sHq5G4JkafLby+iIJZPhvjmLZat9YRp198kT2O+LDD9Euffeb2xqWJG2/UX3j79voHcABGnjdsqA974omWtWeP5WnLlllWsWK6vWPGRPB6HTIkiVvnD0/1227/aurIcmvXjH9d3ZYPP9Q/Ez7eKps13/yBB6x0M3GiZRUurP/7L77o9takEOwwKlTQX+x991mpaPVqyypZUv8Xv/3WgQc891x9sDvusHztyCPDHgNw+KxfX789cqQrW0cF2LfPsk46Sf9GTZpY1pYpcwM7yR9/tNLV9u2W1aaN/hqqVbOsf/6J8YGWLLGspk0DB+BU/hg61OG/gr/16J5p/1oelMcsa9YstzfHc3AdVLSovnQWLw5xh2ef1W9edJELW0dejxNl4D/isu3bt0v58uVl27ZtUg51+5QT/kTly+uS8Ny59lh1ZGAiYwaZWigNQzkiJRhSxtHVEA2JsOSC8c0OwMJD27baSBeJDWhJ49XFrWuv1fYvaKGC0rWQ24nR1lihx+sWv7PatV3YUu/C6vf/Kq2TZXuqy2PtxspDk93pQYbVZqw6oxz2nv/7Rp7551xdPf7zz7ScRIHhXkgyQTblDz9wyqxjvvhC5KKLdGeBbKQU+8WiPz6mp7Vpoy0Y49p3Y39Zv75IZqYeGIJaD/gOpoqgLhPpWXgN5HLnndp2CEMfkSVJ3oJJx5h8jFPy36ZZ8r+bT9WDRpi/ZzrBoBUMg8F5eK1aOuTYNLyOCs4l8SAoP05F6LGHdFZcv8yenWPwVbrCrr1G1YOyYXMRmVSso7TfNSYlB/Q4MR0RzePR3w/DRHPA9ReGUqFNTIgBJZR6oooTWR7AjK0CrFih0WmslmEZzbKs++/XL114odsbl2Zuu01/8W3bOpa1BePHW1aRIvrQDz1keRJWJgsV0m2cOjWfOyKFEHfCaiSFNPzO3+xfUamMXdaqZQeS/vzz5gUSaS4+caV1SDL0j/v771a6wtu5Z0/9nVSqFGalkGJz/fX6i61Rw7LWr7dSxdKlgZVl7MPj1r+/PtjJJ1u+9+ef+v9SunTIVOTJk/Xb2A9lndaQR7z1lv5tMjIs65tvLMsaMUK/UKKEZhqRtWFDIOEK2YfLl7u9RR504EAgve300x09Z/arGTP011FGtlv7jmnr9uZ41q235pO4jDefyQZk3CAtbIsiTsTm8X7qr4XodLFi9rv5s8/0S1gIpyTCOMMSJbQ5OtI6HILVv7ff1tuPP+7NFWy0zMFqE1pEIDshrB9/1M9YUaGQLn3qKGlb5A/ZbZWSB65Zk9TnXrdO2xJg1fmE4w7K0EUnSSGxRO6+W6R1a0lXyLRBhgJ+BUg6RG8ZzD8gByA1B315MMUIY2HdTxR3BBKSkPGI/Tc+4oIpiIMG+btpfO4+KHXq6JsI6b25IEsZvcexH/LjZOBUhVZ4pn0W+i92OWWXyF136RfuvVekQQNXt88rkJSOxJHGjbUlHhL4OaQtF2QiYTIuSktwXogDbJoz7WdPkYlS7BgOVoqpgTzefDi2wKxZSd0u8j4Gtvzg33/1c1ZZwpw5mn2JaiE0HackqllTc/QBU78cvEDD5CHEzaBXL2+d7CMlGNUHuPjHyW6+GNgqUEbxYvLSRTozfOj42nbj8mRAo2b0dV66VEsnRh3WR0quXSLSpIlOuUpziFljUhsuuHG+hPdkisRg3IXu4J98oget0aO17tPnFizQazYocJ8YCfxeVq8WqVpVR3T6HQ4W+UxHxORIBI+B0xG9YcUKHX6AGCvmFtjnI08/rd9AiSwCW5TjdBDzMfCrwTk5qqzRW5+C4NwC5YjQt6/IwoWSzkxg6wwZq8F/yjewhXNjVOzmgUktwMmIlAsDWz6ciGimIXbqJFK2rIvbla5wcocLNcx7/vZbRx8aF0g4oUQWAFpZzJ8vnnD//fr58stFmjfP546LFoksWaLTz+KcHJnqjn+go/SQYWJJIbnztv0JD6CgjQf+fr//LlKpksh3906SKp+8pheg77/P0apZsBCIfSwWm0eMEHn2Wbe3KEVgfNgLLwQucDDRyMcQB8Z7CotLyD6K21tv6WdktKVKjzsT2PrmG033zQXHOPjqq9RtM+QXWPRAoBGTEI88Ug8JGUsWizz3nN7hxRd5jAgBbaMQ3EKvLbTARd9KZCFSkFtu0ZRWvMh69kzbNzuSV3/5RU/0GNjKHwqUMBh43z5tz5aH+d35/DyCnMfAlo8DWyxDdAlGM6NjMDz8cMgT9lihcTWyAFDqt2WLXjS5vQKIKhKk3CNW9eijEWZrnXCCSJkyydg8/2reXAa0/ERKyB6Z9Gsx++IukRBLwLjyYsVERg3bKU0eu1y/cfvt+veibO3aBZKKkLUwZozbW5QikO2KYAeWYNER1qe1njjRRgKaY9laWAwwpe3XXy8pA1NGsPq2Zo0uBIX4doUKWh6N6n5yBxZVrrtOsyNQ5YNjkT2QCB3+cWWJOrtUyCJMEGQ/I7iFZEskkJx1ls56oqATW0wdwr5gypTAAkeawZCB/fszpL4slcYZi0RasBQxHKy3ooF82HJEZmxRGAxs+awUETGuefM0yHDOOW5vWBpDlACBG6wWOByRwKIoKjfQygIJUFhFxSQ9t054TXkk2r40bFjAD7AMMSr1bjxb7pbn7dt9+1r2NUQiYGob2hwBAqftvrwzMOXzyScT86Q+h9c7LvbwHsC00jSvoHDubPW99zS9Aemod9whfmSq0JFda86v44LeWnhA7DdjGq/mUcg8Q2q5mZCWC4Ls5jyG5YjuQZzh44+1PBT9W+02Wgi04kQEqauvvOLdUc0egU4hWABElsm0aRq/R4ISZUG95sCBgR1oyDSc1GZOj5GtldH0/7Tyg2Lrs2UytpAmGbJWkdJV1IGtjRs3SsOGDWUpmrSE0KlTJxkyZIgT20aAnGbTkfJ//8uesox0Z0zQJZdUrhy4KMNYcweztkxS2Hff6d8YC1zXXutOrx/E7FC6huMvmsfnC405sGwJDGxFpnt3ubf4QKkha2TRogw7AOU0VAEhKQueekqkW5VxgSbVCDLw5CokXMchawtlZtgNI8DMVXgHICXko4/0F/zuu1rv6SPYH+J6H0kIBWawRgIn5chmgBtvlJSDaSNh+mwFlyMisMV+dsmH+JVpnfXyy5pFZ78mzUED2elNm7q6jX6qtsbvE4lJ6JGK13aiFqt8CWWIiGTj9XXVVWkXkGB/LQcDW4i+4wIJryFT1UQUbWALQa0uXbqEDWoNGzZMfnBwUhwFlSHWri1SrhzLEL0EafrYsaKbvxlT6SBUniKQiQVTrKYmu7c32iA88IDe7tNHG2rnC6Um27bpkuUxxyRjE/2vQgUpc8EZ8qQ8kD0Rc8MG5x7+zz+14gtxVwwkuO/WnXrDlIWxD1qBCSd4D6JJMBYGcS7ucAw7PZ1ySqBxH0rvwpxTeBGSDQD96rK6A8QHAR/U4uFFlopp2KjLQioQjpOLF+f5NhbpEFvHZDlWlSQXGp6b4wMWz9AKyYYMLWRUYoUNC3cUMUzVxaIkXtO4HLr0Uu2ZSlmrRe+8owvDeLM7UsftD6tW6TlEhmTKqTKBga0ImFJE7IrQmiXPa8mkS7PPFsUa2OrWrZv06NEj5Pc2b94sd911lxxxxBHRPCRFUYaIk5CZMzXQYXqykosQwEFwK7iTsMPQ2sL0FMZo+Q8+kKT58EONq+J/8+67o8izRpNQXMhQZK65Rq6SoXJU4Vl2XNCRLBDRC8UuXbQcAhePb7whktHvPv0GygLMpCLKF+INyCZB2RR6lCHrjRyAC2Y0E8SLHucVyPj0QY8UrLrjGOzY9b7ZwSOygB4DqQaTKtq3D1uOiAAAYl/AcsTk2b5dk+mQjYqs1Ndfz6o2RD80cxDCRESWBsTUoxEvdSyMIG595ZVp2y89rxo1RN58U2/jYIqR22nAnB63LjZLKskWBrYiTO4+7DC9HaJFI/tsUfyBrUGDBknv3r1Dfg9Bra5du0obnKhSQhrHmzJEBDtwrkgegHJERH4QgBw+PCFPgeud++7T20i2mTRJEg7p8+bCDT220OC3QOyvFZtTT5XC9erIS4d6Z1/noo9ePBArwOABVDGjPykSCov+OkmvXgAlYGzuHzEc1sy5OOZFoLyT4oQgDlJRy5XTzuFORXQTBGVyJoMV+2Fzwh2XBQt0OgfqGtHQLVWZlbgIyhEp8ZChhYxDnF6i3R3OLbMHceJkY+dOTZdAiirFBOfpeD1jN4dBE9hnMNs3C5oTonElon2I+u3ZI2lThrg/6+TBkeaMqS+iPlvM2KJYA1vorRXKxIkTZfz48fJshHPR9+3bJ9u3b8/xQQUHtkwZ4oUXurpFFAwXZWgkD7gwS1DWAfp741wAKe0YToTU3ER6+22R5cv1pPfWWyP4ATQfMmOtGNiKDi5qe/aUk2WSnF91in2uF1GGXBhoOYB9BNLe8fcbPVqkXJHdGiEFXEB37OjY5qeLa67RUh0EOHBRaJJpKQ44p0BpitnJoTGNR6Gs6JdfNABgAlxxM//vSFmqV09SlumzNXky0vvzfBtBeAQAcLrDdimJhwRzBOfxWkYWKrJSbb/+GkgLR4NBHJsoZmefreud+DWi9TDalbGPXBY0FMULDwdSx3ao3oSAZnDjeLs/FBbEKb7AVnApIt9YlCXuo9bevXvlhhtukDfffFPKomNiBAYMGCDly5fP/qhbt268m5G6sq6elpQ/yu6XgwMkmhiTh+BsBTmzGJuG+r0EwN8d0+yQOYJac1wIbNyYkKeyY1Sm9QGyUzClsUBII0NQD2kMjqQypGFTVRF5bkNPKVrUku+/1wvpaOHYjv7T6OGPce3ffiti715x4ogRm3XqiDz3nPPbnyYwWfKkk7SMB/thZMZRnNCEBlFDEzHctEm8BptmhmegNR3eRnHDqFszaCcVm8bnDmAidRRRezQgygXVbibWjkALJQ6ys9DL0cRVTR8b+2+DcxnA+zH7GxQPLDLh3A1lnmgHcM89vAa3oewEmeNmakEyShFcgrgLztfLFN8vbWSaSKtWbm+SLwNbed43aHKJHhE4EfNRn07yeGDr8ccfl9atW0tnXGlHqF+/frJt27bsjxUrVsS7GakJJ75ZzVa/mN/c/oyJNVWrurxdlBNKusxYITTCStCkFwSYUMmBxR7EKJC5lYiJOzjHQAPzRo30/DYiLEOM/8LvlFOkkSyU21pPs7+E9m3RJgAi6eX99zUQimFzdqY2VuHNmG1cybBnSsyQVYKyTgQ2kDWJOAzLSxyAZtVNmmiHXbdGwOYDwRYsLCFYbMrCHYkwIIiHyLNpMpXKTDliiD5bwHLExJs1K1BdiIEwqALLhsmcM2ZoFvqAAW5tYkrCcQJZ8PD8856vuk5uShsyyLG/x+Jeio4dNmWIp1adLUXlIPtrRQFJWTjvwjVJntgVglrNmult9tkipwJbH3/8sYwaNUoqVKhgf+DfN998s/0RTvHixaVcuXI5PigEdIvHVVP58vL5WM2G4zREj8LrHWMDsec1q/AJgCFFKC1DbAJlMSbRwSlYVcKJF2BVN+Jextl51mc4tzHp5uqr7U8PrrpZKle27D5bgwZF/uPDhgUmtqGVFs4Z7d4V5kWCK5p0uIBOMLzNEehAGQ8y4niR4gBEjNCIBieqiN6bhmYuwfUVEotQEowBr+a4i5aK2Ac7wlzp4sIuHYZtmHJEpKOGWJFB3AsBeQQQMd+CnIVjO/4Eu3ZpdlyOziFIAzdTSrFDc+xFTgbe5mZ9Cb9izm7J8sILulqLc+e77pJUlH16fPB7vcHAVsRKlAhUHLLPFkXEigF+bMmSJfbtFStW2LfNx4UXXmg999xz1oYNGyJ+vG3bttmPic8UZMQI/LKtZUefh09WRoZlrVnj9kZRWC+/bP+9rLp1LWvv3oQ+1bhxllWkiD7dww8797h33aWPedRRlnXoUIQ/tGKF/lChQpa1ZYtzG5Nudu2yrLJl7d/lq70X2L/SKlUi+5X+9JNlFSumf4a+fYO+cc89+sWaNS1r8+ZEbn3aGTpUf7X4+PJLt7cmRbz0kv5Cixe3rFmzkvrW+/FHy7r/fstq08ayChcO/G3NR4cODu7e5szRB8UTrVplpQUcULAfwv/3mDEh74LfMb6NlwE558AByzr1VP3dHnaYZW3alOsOt96q32za1LL273dpK9PDgAGBfcorr7i9NR6BExhc4OCXMnq0lUp27gycmy3IaKI30mWf75BbbtFfW58+Ib6JNxG+2aWLC1tGyRJNnKiIxKlOrmYTZcqUkSpVqtgfFKesLqpfFrnE/ozeLpiUSx51ww26DIrSWqTaRNR1PfaJO5ieh0k7qH5E2eAVV8T3mCtXaj9PM4U54r6x2XOMW0c4PpFCwtz7bt3s184NmwfI6/8bbLfYw98iv7kcuA/KUlEBi8wSTGi3/f57IP0OLxY2K3UUyniQ/Y7SXdyeNi2QFU8xuv123Z8gXQpTs/AaxvvCYXivYPUXAwnxgb9d7gpytAo89VT9QAuA7AbbTmZrIU0JEx7SAQ4o+P/F/zuy8s48M2Q5IlrtoBwR2XHkDGQe4nWOxEj86nNM1UZ9Ipo/mZLgiNO0KRYoZd69WzPiMWQevbdSuYMDkrGyJ26G06GDvuHRxBIntXPmpMzo959/1mNL/Rr7pNHaBZoN6ejBJD36bKEKgRlbFBHLA5ixFUa3bnYk+sQGK7i64xevvx7IkNm9O+FPd999+nRFi1rWpEnxPVavXvpYJ51kWZmZUfxg9+76gw8+GN8GkGVNnaq/y5IlrdGf7bJvYrVv0aLQd1+3zrIaNtQfQaZJ9ksOGYNYfcc3evRI5v9B2mZCNGrEpDhH4EVdvbr+Um+80bG/07Rpmi1x+un22ytPRlbt2pZ1xRWW9f77lrV0qZXY9LDy5fVJf/jBSivIxjC/7BAHmeXL9dtI3li71pUtTDmDB+eTWYq/gUmTu/BCl7Yw/eDXbrLjU/0Dp8JffRXBLwUnL//7n/4QzilTxB136P/Sde3m6o1OndzeJN+ZP19/dSVKhEgoRdzAvNiiqBQjf4kmTpSB/4jLtm/fbk9HRCN59tsKcvTRsurv9VJHVmVn1NSu7fZGUb7QOwRNkJcvF3nxRe3QmkBowYYkHzS0xgLX1Kn69NFasECkaVMdjITeXSeeGMUGII0QnR2x1N6+ffRPTgHYHeMP8e+/Yr0zSDp93stuPIrJSp9/nvOuaJ91yim6ioXsEmSdZA+WQLMtjLbE6uDcuTq1kxLWu+bYY7UvUKdO2ncrHVomJRSytky/PjRZN53Fo9gtIRHFZGRh1Tx3X2K8V0xGFt5HyHpF9kTCYboD+t7hTYs+mhGnxqbIQBzsi9Doafp0bWCWC4bx/fGHJnZdf70rW5kycGzAIRkZI/37izzySK47fPqpnkBgMg0qBOrXd2lL04+ZtIrk/miHxPjpdBjZaWb47auvFjD8Cm/8tm31RBTTby6+WPwOWdzol/rZaW/JReNvwvQ0TcOnqN4rlStrK8CQh43GjXUqPc4bzHhdSilRxYksD2DGVph+FCVLWq/KLXYg+oQT3N4gitg77+jqQbVqWmCfYFjoOv74QNZILIsWl1wSY5n6X3/pD5YubVn79kX/xJTXs8/q77RtW2v2bG1dhn8GZ+Rh93DBBfr1SpV0RSvbjBmBJkGffebG/0HawdvAZAEhi5IcYPrDVayoqTwFZEDMnWtZr76q7wu8J3JnDuBhunbVzGe0uIoqK9VJZmf99NNWWjI7roceyrcH0ZlnJn3LUgra+JiWZuefH6JnJs5N6tTROzz6qEtbSakM56Y4HprTkcqVLWvYsAL2vdgvmDv7vKlwcPvZTUdlpXajdzJFDccD/PpQFJPHRRfpN3HuTCkpmjhRGi0V+gyW//fskc8zdMWC0xB9BGOLsRq/fr0WhicYFlvRNwO9DLBogX5LIYZOhYUJ31gcQ7bCk09G+eSmvxaa0GCiGcUPzdKQ8jN1qjQv8m921sKdd2omCtxzj/ahwa/8q6+CsvSwNI/piljxxE6DO46kwNSewYP1Nnqc4f1EcUITGvTtwzLtZZfpazoLzmIXLdJsB7TiQssSrIzfdpu+LzZvRr9PnQyKNnOYtIekUnwP98F9k5KdlRv6gCCNBn2Msqagph0zHfHrr0N+2yTnjR8vsnVrErcrxRLj8Htcs0Zf6x98ECIxcMAALQPAiUPfvi5tKaUynJviZYZdXsuWIps26a4crfZWaSFKXkhjQ98k3BknP+4XFcUsu/3ssZlS6Z8p+g9ORIwJMnkBbTfzYJ8tCsLAllf984+sk2oy2Wpn/xOlSOQTuGh5+GG9ja7fuWtgEqB6dZHRo0XKl9dSwmuvjfx8wEz57tFDTz5iOnKncvfTZENpJ67I4f337dHgyLzFxfmHH2qsFBOyYcgQHSqRDVGVmTM1bzsJQVUKQEWPuT5EzAJ/BooDorbDh2uEavJkWXHPq/YFOtYNUDGF0kFc93zyici6dToWHEM1EJxHSTaCW9gnYoJ8q1YeKQ81TeMRdUCZcDrCvg1RFrxBli7N820E6RGMQXkWynopOjju33STBhMwLwSLXmXL5roTosLPPae30TIBEQiiBEHpGKoMsVaB3Tre1+i4gIWJPOepuAN29Pj8zTd6kuNTaCMBZxy9QVeb8UbEojfF1EAeQjaQx8oiYJoPkeUBLEUM4YUXrLfkeju78rjj3N4YiqlbcZOs0b5PPJG0p8XI+iJF9Gn79y/4/hMn6n3xMwsXRvlke/ZoN0c8AOqAyDkjR+rvtUYN+7X0zDOBcipTmvjkk7l+ZtYsnSKAb378sUsbnt4OHrSsM87QP0GDBpa1caPbW+RfaB4+fLhlXXfyAquRLMhTWoiXOgZdYD+HafGYl+Bp27dbVpkyuvHY8aaz9u2t/CbimGoklI5SdAYODJQ/jR0b5k7nnKN3wiQF12pyKR3hVNFUY+MDw1dCDscxJz1lyyZ4mkdioPS3ShX9X5j8wPd6Awcsisn69YHXzJYtub65enVgp4fhLJRyWIqYCv75Rz4XLSNiNZEPFSmi3VoBtTDbtiXladE38c039TYyfT76KPx9cSRAH0tA5sPhh0f5ZEgNQ80DJhr83//FvtGUV+fO2mV17VqRMWPk9ttFGjbUqiyUIyIjz/ztbEhvQJrQgQOa54/0IUo6ZAUhyQiLskhGwZ8hVRsDOw0ZViNHBkoFkbiIMsNBPzWWhdJYCskhOa7YX3LfHXvslXC8F9AUHg2xMS2+wJHybsMLY+dOkSOO0A1OZ6YcEelE+ZQjjhmjfeYpMijfRMk6ICErZCL1999rJgzOUQYOdKkml9IVMrWmTAkkCmK4R4sWIi+/nKPaXFNtTzhBKx4wbMP0YfAJVMVhsAyStI7flpW6hdRhiglOh02yG7L/csDJAjKg8RqZPduNzSMPYWDLozbOWi0T5RT7NssQfQpjYHAUR6OQl15K2tP26iVy7716GwEQXPyFgnNbTNLDyQXaGsScZ42zZ54cO1/Oil5bMHiwfdGOEz/8mjF5D8HLHL9y00ioQgWRt97i38NFmE6KvmelS4uMGydy331ub5E3bd+es1QQw/IQ0HjtNZ0iZSoMcKH+zae7ZXPDY+W3/a1kwIor5PSOlv379Q2sIuB9CTfcwPcngu+ASbohGmkdeaQG8jH59Ycfkr95frRkicgll2hwAIeOkAOZ0YPxjjv0du/eXJAi1xaA8PpEDAITaTE5Ef9u1y6w77fvNHSoSKlSGv3yWWsFc3qM/7+is/7Uf7C/VmLKEXE8ZZ8tysLAlkeNmttIDkkRafW/3SzJ9iscmM18bQS2kJKQJJgmjEw/nMeimTymygfDya/prYVsIDRfjhr7ayWWaS6NCOSGDfa14IoV2p8Cca9sGNNusgMR/Yrpj0lOwgo0zskB/dCGDXN7i9yHixfsMrDfadNGA4BduujKPVpjIPaDdYBbbhH54gtd7cbX8fvrckkpKf/pO5plgm+++674CpaY8T+DCPVVV7m9Ne5DgzT8sZHOiAyiENcpJmsLDf8pf0gERBIcTjEwbwGt3ELGTnF8WLBAm3KaYwaRS1AlgMUfvF6R2YSFVsQnnnhCk8/t/YTpBYfVWrx2/dZf6/TMQLCFgS1HAlshG8izzxZlYWDLizZskM93dbJvXtitiNtbQ/FAuh06siM9wXT8TgL05kX/TRwIcLKLfr0YMmN8/LHI3Lma4IMJe1HDxEdzsEb9IzmveXO9SsHFX1ZNKao+czTBRoQSafqIYJ51lsiVV7q2uZT3rf/AA4EsSkwfTSfolRtcKoh9zRlnBKZk4aWLC5vrrtP9ESa4YZ+EjC0ENTD/IAe8FxCxN9H47KV9HzBN45FSg4geBbK2CpiOiLg+dm8UGgLCGKiA7BfEqxAIDNkLfvVq7d4NzzyjE0mIXIZzVbTCwO4cHRjwXn/oId3d28fMG2/Uc0ykb2JRwAe1/SifRqcOOOP/Vur5PxY1mCHpyGREnD/kGTrAjC0yLA9g8/icNn87xSoi++1eePPnu7015Fgj8NKlLWvDhqQ3YEYTa9O3Eg2W9+0LfG3AgBgfGM3J8QBHHunwFlMOb7yhv+cWLUI3+X3+ef1+uXKWtWKFG1tIBTST79xZ/0R161rWunVWSs/LmDrVsp56yrI6drSskiVzNnvHR506lnXllZY1ZIhlLVsWY0deNLw27wkMsPA6dLo1v4wpU9zeGu/Ai8Xsu3BQCvGnxuwM3OX7713ZQl947LHAMIV8X16XX653bNNGf7lEHoNTnI8+sqzKlfWlWriwZd13n2XtWbDcssqX1y/iAONxo0cHBshkjvhM/3HMMW5vlu/hcG/mIy1Zkuub//6r38CxFidelFKiiRMxHciDvv58nxyUotKizBJp0qSh25tD8UKNAJrIYPnp2Wf1I0mwgos+Nm3bikyerJkjWPVAY2tUrKHNRkxYhpgc6J6NJkNYjkcPrWOPDXwP9aWmORp6bNWp49pmUmjIrkOyHd5z+HM1aOCDJucxwhwJfARDP9dTT9U+I/iMDK242kuZVFRkweI9gXTTV14RT8MLANkGyMDEjpgU3hQ4QK1bp722ch1L8KdGGT36CSILCb0FKSckXj38sN5+4w3ttR0SunXjdYg336uv6i+XyGPw8rzsMt0VYIjIiBEiTz+NoSJ15b1bh8uJT56tJbRI7cIxwOtliGeIZPydVRrHMsS4lSih/RenT9esLZxPZUPZKhpvIl0OJavMjktflgcwYyunLg3n2IHnR9tymTJlfPttYDUBaVRJ9uOPllWkiG6C+YxkoJiX1ZB6gQf54QeHt5Ty6N5df9c33xz4GlbckYKHryM9hiPbPW3ePMuqVi1vBlOqfVSsaFldu1rWq69a1pw5CXxZfvdd4Em//tryLPwCmjXT7XztNbe3xnt69dLfza23hj1u4dtVq3IRPvfL6v77A2+Bhx/O5874xR19tN4Rv28iHxU7mKzNjIxM67aG31g7pLRWCoTI8vSK//s/3ebPP7csq1OnOE+4Kdgtt+ivs0+fEN9s21a/OWyYC1tGXokTZeA/bgfXtm/fLuXLl5dt27ZJuTSv+9+2TaRaxf2y3yomcx/5TJr2v9jtTSIn4G2G1XosM2AqURKnJBrot4x+NoCBBOg5XqxYDA/077+6GoLUky1bwjT0IMeguyqWMNGkCI2IsGyFVXek22GFas6cXEtX5NXm6StXSspCX/f69XP1gEskZDJiP4pmXDNnagM6r0GmDEZ9YbIXehyVL+/2FnkLGmih11bduiLLluVJ50MDaSR14TCDpK727V3bUs/ARHtMkDOJikgA79u3gP5u6FOE1x4yGZBGSeQTeO9jcu777+u/GxRaJu9k9pLT7z9O5MknxWtwjMfuDEmRGIBS8f9qaFbq1Kk6NYXi8uGH2koW2ak4vOaAyTNIXcUOMYmVMeStOBFLET0GE88Q1Po/mSdNT63h9uaQU3DC/thjImeeqbUV2PHWqpXUTUAZIg666L+M68GYglrBZYi4YGNQK/FQw1Wvnsjy5SJffaUlPPfdp9/DwZtBLV9AbKNJE7e3IoWgC/1PP+kUpCuu0P1S0qJqEXrrrUBJMYNaeaEpNI4hGPeK4KSZbJUF018R98KEUZQjpntgCwMXsDiFi3ycUuAaDjGrsDA5xkywwPkHg1rkMxUrigweLNKtmzaZX7qsvpwhP8o1Tw2WF06ZLhU6BrVn8ABzeozTtIp712hQC1EuD5dO+rGBPDq7YOEjx4RwTkYkTkX0ns8/0YkfF8nnrBFONci6OfFEHRdmpnslGSaUoQ+OGUgV15EbDQQo8XBShLFX8N57GqFE+g9GzeV7VUOUwpAx+sknmrU4caI2HPISjKH97DO9fcMNbm+NNyGoZY4jo0blOx0RgS336wvcg2lxiI8iqIX4LVrNFbj7x3g5vA6bNRO5+eYkbSmR87CbQHI6em9lSKYMlmukaad6MmrEPvFify27ZaAJsBxxhK5sUdwaN9biBVzHoM1m2MmI6XywSHMMbHnIjh0i34/VP8lFFcaLVKni9iaRk7DEasZtDxqkGTguiKtvLJZIcBEJbByfPCawhbJE/P5xQYjaUjYBpnSGFLjXXtPb6KI9bZp4BtKMsIiBwSHBQx8o73AV+PrrkN/GYQaxSyR1YX5GOsLsAQT4ECdFhgI+X355AT+EDDiTMYjSddQKE/lYmTJagvvzdzulSZFFsuZQNTn/0uJ2NteGDd4oE86x7msCW2wc7xic8pqsLXR2yQEDWhD1Rw3oqlVubB55AK+KPOS770T27S8kjWWBtGiW6fbmUCJgPNjJJ+vyqwf7AxQIF447d4pUrarjSSg5GjbU146BjD9MgSFKd1ddpXUqqNNCSgsaVboNq8XobQRIq4lrFGSK69JFfz+oLUH0KhfE8M8+O5C1lY4LnhgCh+nG+F2gLRmmRRb4+kNqC660L74457GDyOfanVVO/v5isdwnA6SwHJRPP9UCl48/djdRB3EsJEiWLSty/PEMbCWK/bsNFdhC/1lT6YSsLUpLDGx5yBdfSHYZYkZTliGmLPS6ADQOWLJEfMUsR512GrOFku2mmwK9zXDRQkQaFEFmCnrNLV2qZX9ulyGg9xcadeMKB8E2Cg+LJOgEDIja5FOOiHMkt/+0yW6cjYw1JOnipfTDD9qms0Ao0Z08WSNhzz+fhC0lSq6S554uA25YJr/J8dKy6D92QOmyy7TNhlvJOqYMEW1R7d5PCNYDsnYp8YEtYJ+ttMcrU49AyxysyAH7a6W4k07Ss9WDBwOliX4LbLEMMfmw8v7rryJjxnivSTaRm9CYffhwfV9g+X7IEHe3x5SAoV4M9TMUWTlimD5byNjCsBPECjHNNx2g5zSSu3HxVqmSyIQJeupQIGRUmzGJ/frp4BGiVPT883LMYVvljwNHyuOtRtr7CAzgatpUu30kOwhuAlt2GeLWrYGF61xDMSg+phQRA9rzJGgH99mitMTAlkfgWhXBrYZFV8jR8hcDW+mStYUOsP/9J76AA/Xvv+ttBrbc0batNpwhopwwSt0sFCCjcf589yISI0fqbTaNj4yZZoLUpO3b83wb073NIScdyhFRkYkJkLNmidSoITJpUhRt2lCmjpQVlK+bABdRKsKiwZAhUizjoDw44wL568WJdjYPdiGYoIihq4sXJ2dTEE+eMiUosGUCK/Xra2SaHE3yxe4N/vgj1zeZsZX2GNjyiM8/188XHRohdjeO//3P5S2ihMJFGJah0RfGBLm8Dhcd6NmBCS9167q9NUREOd1zj/YT2rVLSwDRvD3ZMLYOQzawj2cfwsjgmIIP/N6wylfAdMRUtnChZmYhOw3JVqgoRE/kiH/4hRf09ksvac8ZolSGN8udd9o3mz7eXaZ8vUlefFGrcJHl2KKFyMsv66l2Iv38s+6+EHA5/HD213KtHNEEtpAth8V4SjsMbHkAxpaa1hIXZX6qY2GZPp76TEALHS/9UF/BMkQi8jKUIn74oUjlynphgVKsZELg/5139DaztWLL2gpTjohvo60j/qx+a00ZqTlz9Dp92TId+PnLL1HOCOnTRwfTIGXE/D6JUt0TT2j94bp1Uvi2m+23wezZusaBShj8G61JE3maHVyGaM8KYWDLncAWsuPM9TMmw1LaYWDLA7BDRBpr3Sq7pbX8oSuXbMyd+o45RnuL4GLo0UfF8xjYIiKvq11bs6ZM1grGDSdzH4moS4UKIpdckrznTaU+W/h7IfUhlypVRDp00NtffSUpZ/p0/f9bu1azTJABElViNH5vaDBUpIjIwIGcxEnpA5mJaOuBhY0RI+zhCciaGj9eh9Ni8AIGeiOZB8PIQ+xenO2vBQxsJS2wlaeXGvtspTVGTzxUhnjhEXNZhphuTEALB2MsMXkVLtZQ5oCTZnS0JSLyqnPOCUwO7dlTZM2a5DaNv+oqzbymyKF0E81TUD6C+rs0KkfE/y4mqW3erI2RMVSzevUoHgAlt7ffrrfvuIPnkJSeC8UPPqi3b77Z3ucjtoteW/PmiXTurMmMuAveY062YEJPPGSDIR8B72PZsyeQHsaJiAmB2BUuR9avF1m+PNc32WcrrTGw5TKcj3z9td6+qNwPeoON49MHerBcdJEuOTzyiHg+WwsXH+jkS0TkZc8+K9KypciGDRpoQmZsIqFht+kpwDLE6CHboksXvW1OinI5/3z9jCbNyGxKBcj0OPNMkR07dM1o3LgYek2jiRAWntBp/qGHErSlRB73wAMa4NqyRaRXr+xUnjp1dNf80UdapY5EntatRe6/X1vBOHV6jIAZknXtRWo09UKgvlat+J+AQibpmRaWecoRmbGV1hjYchlSZTGutGZNkbYbv9UvMrCVXhDQwtISlqG9usLAMkQi8tuZ7/Dh2kUY+y/TVDtR3ntPL2Ywzo7H8PjKEdFnK099iV6gogQF3wrTistXMDwTyYVI8DjrLK0mRNlU1AFVMw30mWe48ETpq2hRkaFDRYoX1zcT9slZcIp92WWavYUqceyqBwzQ5B4zzTAhZYgsCXavgfzcue4MkCFXMbDllTLECywpND8rdZVp5OmlWTORbt30dv/+4jk4A0AEFhjYIiK/QENh9BsCLM/nmQ3ukIMHRQYN0tvM1opdx44akFy6VDupp3A5IrJHLr5Yy6OQtI2+YYjBRq1vX50C2ratyOWXJ2BLiXx2Po1m8oCu8diXBKlWTeTTTzWojATH+fN1YAMqedHrOJbTY7Puy/5aHglsoXl8xYp6XEYkk9IKA1suQgND0wT1olM2iWzfrkXajRu7vWmUbAho4W+PfOnffxdPmTFDU7vLl9f8bSIiv0BJCiIHOMnt3l1rvpz2/fciK1dqh/MLL3T+8dNF6dKBxZMwKVldu+rnCRP0sORHaMV25ZV6UYwWcEgsLFYshgd64w39YWSFvPoqhw4RmYAWolWIVOENFqIMHWXNiHlcfbVmgL7yig5tMEGqSCGGhd54SJREKWL2F4GBraQEtv78M9dAAOwP2WcrbfEo6KKJE/XEDCsI7cpmjSXFKA+k0VJ6wSTMK67wZtaWOdJjdjK6NRIR+QVOct95R1dxFy0SueUW558Do7cAF1E8fsfn3HPz7bOFdT9cgCJOiSGAfvPccyI33aQX07feqtVSMR1Wx4wR6d1bbyNDBb2FiEj79WEyLgLlkyZp1CoEJPUMHizyww8i9etrcheyrq69VmdYRHN6jKbxqIS0d0yzZukXGdhKKBwL0NMMfdLyzN5in620xcCWB8oQkVpf+L9/9R8sQ0xfaPqKAzJOWH/9VTyD/bWIyM9wBTNsmGa0fPihfjhl2TLt5wIYwUXxQdMpBCNRNrp6dcqUIyKQ9fDDIvfco//u10+vt2NKssJVnGkUhMEIeDAiCkCSwPPP6228P/7NusYKAcEsVD5jkC52PQh2oYo9kj5+efpr4XkQaSlTRqRRIyf+TygM7DtNEUmeQhdmbKUtBrZcgqA+arwBVRLZo2HZdDa9D8TIiwacAXsBeneYzpoMbBGRX7VrF8iGxTh4TJFzAnprIWqB/lBsIxC/6tUDNSZhsrZMYAtrQDhEeR1eHnfeGejx/tRT+hFTX2mMg8T0SJTUduig2YhsUE2UF/odYuQoAk0IAOPCKwzEoRBo/vlnkSZNRNas0XJFtL/FYN1QUOloTo/z9NdCYIWlwe43kJ85M/ETkclT+K5zyeTJIhs36uhZnJswsEW2Bx/UfGY0a0cKtdtwlEfxOvK0ufpERH4fB4+phbgiQb8tdO6OB/aNZvIWm8Y7Px0xTGALpYhYB8L1KoJbXoakKiTyvfyy/hutsGJOsNq9W383y5drEPWLL2JszkWUBhDwxf4Z9WpI6Xn66YjWP1C9dt99WkCBRvO4LPv447yDWnGKjkNAw4a6P7Kxv5Y3AluofkJbACwALF7sxqaRSxjYcrkMEY1Q7f4KJk2WpYjpDQEkNDs2WVshRp4nVXCeNVeFicjPcKWCcXQoTZw+XRcS4oHACzJoMF7LBGPIuT5bWOAJ0ewfhyI/lCPioheDCt99V5M30PYHfbVigqwDZJ3gAr1SJZHRo3VllIjCq11b5LXX9Pajj0bUcwnTSQcM0GDJkUeKbNokctllultatSqfMkRgYMuVwBYuobdtC/oGEgSaN9fb7LOVVhjYcmkFz5yM2WWIeDeaXhLM2CKMpcdKA7KlcGLvJvbXIqJUUreuRhpMJ29zdRJP0/hrrsnqHEyOwHkQMoSRURfm72MCW2ggv2+feA6yyTAg85NP9KWBzA/MFogZgrBYEcWDoY8Fy16JItOjh+4wUIqIIU0R7jAwjwGt/lBCjMRI7GvQe8tUn+cJbOGLDGwlVdWqmjGHXz3+VjmYvwH7bKUVBrZcgL7gWOTFojEmaWRna9WsKVK+vNubR26rUydQ1uJm1haCrXPn6vK4/UIlIkoBuMi58Ua9feWVIuvXR/8Y6NGFwD/2j9dd5/gmpjX8Tk3WVpgOzscdJ1Krlsj27SITJoinoNIVbbC++UakRAmRr77KWsSMFVK9kEICCMqinJaIIt+fvPWWjqBHl/goJo8jjoyYMmIjyA7C/galxSedpJduyMTMPj1eskQTFRAFQwSMkgLHgnwbyDNjK60wsOViGSIqF+xFXpYhUm4o8McZ8dSp7jURGTcusGzFkgciSiUvvijSrJnIunVa4hVtg1ks20OnTiINGiRkE9OaKe1EyV2Ips+4oEQrB6+VI27dqhkcSLZGQ+rvvxc5++w4HnDixMC0TVxhIxBLRNGn9pgMW2TqRjl5HHEqNIrHYQOliqZpPIJdaOGVIzMIJXDsfed+ny1mbKUlBraSDOfO6PcJ2St4bBxPuSF775Zb3M3aYhkiEaUqXJ0MH65l31g8GDgw8p9FKQtmwoPJ/CJnnXCCLqhs3hy4igxTjoiMKLR4cBump51yiq5HISMfa0MnnxzHA86fr/WMCOxdeqn2CCKi2GDMIQLDpl9dlCNV0aKxTx+R2bP1fQ45MjFZhuh6YCvHpVLLlpqthxGXWMCitMDAVpLhjYfmg+XK6XRwGwNbFMo994iULq1NjlHTkEw4OpiMLQa2iCgVYbweluDh3ntFZsyI7OeQIoSxxigbjysdh8LCVJ3OnfOdjoiKPPRRx5/il1/EVTivw/ag6gUVTz/9FLjgign+p/D/v2WLSJs2Wo6INDUiih0WMLDfRik59vkxwAREZGSi8hCBrmwMbLkCv24cLhC7wsDYbEiZNb0IZ850a/MoyXiUdKkM8ZxzdKHYZkoRGdiiYDg7vu22QNZWtKUy8UAfAjSCK1VKV86JiFLRTTdp2RtG2HXvrg2SCmJKWjDB1h5rTAktR0SfrRBZy/jVm7u4WY6IafKm5w5mE0yerMkCMUNGINLRFi3SMlekpCHDkIjig7pBBInh9dcDC7hRQiIQ3po5hoUzsOUK7BoxvTJkOaLps8VyxLTBwFYS4bzMBLay01cx9QcnL8AeW5Tb3XeLlC2rqw2YhJTsMkQsQWdHYImIUgyuTN57T8fCL1gg0rt3/vdHhvWkSVqXgsAWJQ6aVeH4g3Mkk9kephwRgS03KvaxWQhqIXsDgxwR1GrSJI4HxP8EhhHggZDaj1Fs1as7uMVEaQ7lMjffHJhoi4bv8cJCMErecDyJK6pNCemzxQbyaYOBrSRCRRnSJFFdduaZWV/87z9tDoHgBUb8EAVDj5E77tDbmOSSrKwt9tcionTaz370kV6UYDX/k0/C3/edd/Qzxt4hGEaJg1KS007LdzoirlFxt5Ur9RwrmVC5irUfDBBGv+iffxapXz/OB33ySZEPP9TA6Wef6YADInLWs89qJHrFCpHbb4//8UxG0BFH6A6JvDUZkRlbaYOBrSQy2Vo4H87OKg8uQ8yR00qUBUX85cuLzJ0rMmJE4p9v717NSDAr5kREqQ5dvh94QG/fcIOm4OS2Z4/IkCF6m03jk+Pcc/MNbGF4sGnFlcxyRPSzRwNptMI69ljtqYWZL3FBQPWhhwJlUjz+EiUGMgyGDtW+dfgcZv8SMZYheiJj688/tatANvP3QDZ2lMMCyJ8Y2HKzDBFMej3LECkcjFe66y69/cgjiR//hDHIuIDDWTpXi4koXSArtm1bke3bRXr0yHWGLJpBs3WrNldh0CE50JDU1Jig3CefckRMnE5GOSLa8uDPj5cJyhDRSBpJf3Efd3v21Nt33qnBVSJKHPSPRbsPuP56HWsaKwa2XIXyb6z/49IFLYKzoYy7Rg09MMya5eIWUrIwsJUkKO9Fg1Fkap11VtA3OBGRIoFUaYx/wvjvjz9OThkiajyYRUhE6QLdyLF/xRnytGm6kBDsrbcCF0GcUJccaNHQurXeDjMdGOdUaMWFzg7z5iV2c5DYgQyx3bu1pcSYMdoKKy44OTz/fG0ajww1lEkRUeI99pjWEa9fr4NEYo2MM7DlKhyOTTki+2ylN56ZJYnJ1sJkcGTAZmNgiyKBM+e+ffX2o4+KHDyYuOdify0iSlfIxho0SG8PGCAyYYLexmrv1Kka/ELDYUoeM/rw669DfhstSk0CXSLLEYcPF7nwQp35gywxBLkwODguyABEfwpki+ACbNgw7a9FRImHiPgHH+h+HSmfeJNHC83nzRAwBra810CefbbSCgNbbpYhohE4MnCApYhUkFtvFalaVQ+gOBAnwqZN2hHXZGwREaWbiy8WufZaPXhffrk2Unr7bf1e166cUudWny3UAIbpkxI8HTEREOu87DLtBHDFFSKffurAwGCUuuK1hgVODCJARhobTxMlF4JRDz+st2+5RWTVquh+HlPLoW5dB2qSyfEG8szYSisMbCUBen6jbx1OgkyTUxumcSCfvWhRkcMPd3ELyRdwwnvvvXr78cd12dhpaBaCizmkZsfdCZeIyKcGDtQJVxjhjkgGJtUBm8YnH45HDRvqYBOTURyiFRcSnUzbBye9+KJWn+LQiGolzA9Agkdc8GC4iEawDmn8CGpxyiaRO/r105JnZFD26hVdSaJZDG7VKmGbR5FnbGGdAP0P82RszZ6d2GoX8gQGtpLAZGt16qQp83nKEBs3duAsidICzqrRCHHpUh1L7zSWIRIRabABU+qKFdNGSjt26LEao/AoudDr0ZQjhplehkQJDLaEkSOdeVpc26Ly38xuueceHVboSHs1RMuQBob/N5Q/sYSJyD24BkMlBMasYn9vytEjwf5anlCtmnYSwH77jz+CvoHEESQGYGHEVElRymJgKwlCliHCv//qZ5YhUqTQ0AMrS/DEE9ps1ik4GjCwRUQUWOl97rnAvzGpjgM13C1H/PbbsJOBnSxHxOEQbS3N/AAcbp9+2qE//1dfBXpmIsBlJj8SkXtwLfbUU4HJpJGmfjKw5e0+W1iJOPJIvc0+WymPga0EQ1IWShFRbZjn3IWN4ykWqIlAycLKldGtKhVk4UKRZcs0Q6F9e+cel4jIr267TZvFH3MMm8a7qV07kYoVtd8ZmviHgMGC8OuvWkEaK8TNUHH6wgv675dfFnngAYeCWn/+qc26TF0jJh4TkTfg/dihg/by69kzbBA9G7KAzChWBra820CefbbSBgNbCYYhG4CJPZggngMDWxQLpErjLBuwurRnjzOPO3asfj7xxFyjO4mI0hSiGe+9JzJ9ugZWyB1YHcRY6XymI9aqJdK2bSApKtZ+7ldeKfLOO4E/vWOxJyxGYYUTvVVxUvjKK8wAJPISZPegzQdK1yZP1qh2fubM0eAXaqHr1EnWVlIEga0cbdI4GTFtMLDlVhkisBSRYoWJXfXq6bL0W28585gsQyQiIq8qoM9WvOWIqOzHkMKPP9aWO2h95ViS3s6dIl266DG7WTORESPYW5XIizCoAiXCgEVkk5FVUBkig9Suw58Bu9V163Q+W45vmIytaAYDkO8wsJVA//2nU2DxJjPtIbJt2iSyYYPeZmCLooVywYce0tto/BFmBHrEMClk4kS9zcAWERF5zZlnauYWxkybhcFcunbVzzicbd4c+UPjEIpkKsTMMMEaDegvvdSh7UZGR/fuekKIDsfoE5YnhZ+IPAOTEc86S6PdSOFEKmconIjoKSVLirRsGaIcEYsJuBjHQSFHxItSDQNbSShDPPVUkUqVwpQhIuuGZV8Ui6uuEjnsMJH163VUUzx+/13n4+KFyj4BRETkNeXK6QlVPuWIGICFPsGIJX3zTWQPu22bxsyQtIzTse++0+Qqx9x9twaz0EYAkTOM7iIi70L21bvvavk5+uKZpvK5sXG8P/psYbWiaVO9zT5bKY2BrQRiGSIlFFauH35Ybz/7rI6jj7cM8bTTRAoXdmb7iIiInGTS3x0qR0QvesTKpkwRqVBBZNy4QOzMEW+8EejTM3SoSJs2Dj44ESUMmvaZRWOMRUWAKxii57Nm6W0GtrzfQJ59ttICA1sJsmSJ7gPRh9BM6smBjePJCZiu1KSJlraiEW2s2F+LiIj8EtjCZERkK+cT2PrhB21tFc7q1ToADdVEVatq+aKjcacxY0R699bbTz4pcsklDj44ESVct27aeA/tOlCSiCmIxvz5OrwJaZ6NG7u5lRQisIVr8BwVpJyMmBYY2EpwGeLJJ+sJUx4MbJETUDPev7/efv55ka1bo38MlCBOm6a3GdgiIiKvwuQx9LNBA2CU94WAdiq4zkR7nO+/D/0wS5eKnHSS9oWuXVvk558DC/qOmD1bA1nI6kDbgH79HHxwIkpaSSKyLqtX152F6W0bnPmD2mdkMZAnYK0fLQwRc5w7N+gbzNhKC3wnulGGGFyKyMAWxQsdblE7jqBWQaOJQ/npJz35btSIvT+IiMgf0xHD9NnCtWh+5Yg4/WrXTmTxYm1TOXmyw10h1q7VJl1oD4CUsHfe4cQ0Ir+qUkVk0CC9/cILIr/8orfZX8uTEGNs3TpEOaIJbC1bJrJliyvbRonHwFYCLF+ubyacx5gJPTkgjIzlQmCPLYoXemI98ojefuml6EZBBZchnnGG89tGRESUiMDW2LEiu3eHvIsJbCGpK7h6CIMJ27cXWbVK14MQ1GrY0MFtw/Zg+3AiiLQxRNYwxZiI/AsjU6++WjNFkYGJGmdORPRXny00UTSL9yxHTFkMbCWAWSFEmnuNGiHugLps7BwxgS5knSJRlC68UGfcoqwQK0rRwMUBsAyRiIi8Dse6+vV1kXD8+JB3OfZYrVrE9ae5Cyru0R5iwwa9Fp00SftDOyYzUy96MWUY53ejR4cYiU1EvoSKCEyyR6onJp0yY8t/DeTZZyvlMbCVAJjojHOuiMoQmZ5OTuXePvqo3h44UM/cI4FV5QULNOvrlFMSuolERERxw3lTAdMRcUg0GfNYbJwwQaRjR63YP/FE/TcqjBz14IPahwITi0eOZENpolRSrpzI++/r7bff1p0J3uto6keectxxgXbWWO/Pxj5bKY+BrQS48UadiojPIZnG8SxDJCeh/AHL0Lt2iTz3XHRliDgKoNsiERGR15nA1jffaI/IfMoRR4wQOftsPTQiuIVpiY4f7nDBO2CA3n73Xa13JKLUcuqpIrfdFvg3glosNfYc9PpHggmKo6ZPD/oGM7ZSHgNbCVxQRCA/JE5EpES96B57TG+/9po2sI00sMUyRCIi8gs0ZUd0av16Lf0LAQ3ikZWFckRMSMTaD+JgpUs7vC0TJ4pcf30ga+vKKx1+AiLyjKefDmRjsgzRX+WIJmMLEy6Dmy9SymBgyw2ciEiJgmVp7M3Re+SZZwruB2KajzCwRUREfoGVw7POyrccsUgRke7d9XaPHiKffaatIhyFnqnocXnwoE4oNi0BiCg1lSql9c3YuaDXFvknsIXGi5Ura5bv3LlubRolEANbyYY3E3oaAUsRKZFZW2++qaOfwkEq7saNImXLBo4AREREfpqO+PXXYe/y7LMif/wh8uGH+WTRxwrHz86ddXR8mzZajojmXkSU2po3F/n4Yx2tSp4PbKEkMfsaiX22UhqPwMmG5lvIiTcd5omchuwr1GDgdWZ6fuRXhogxUY6f8RMRESVQp06aloX2Dv/9F/IuONXChETH4004vqKJ16JFOkL+q69ESpZ0+EmIiCgWaDmMwwO6sqxYEfQNUz6KEvKlS1mSmGIY2Eo201/riCN0Eh1RIrO2Bg3SyYehsL8WERH5VYUKujBTQNaW47D8f911IpMn66S0b7/VbsVEROQJWGdo2VJv52jDaDK2kHHXsKHesWJFbQ+E6fCoW7/zTk33/eADvVaaPVunzaOFC3laEbc3IG37a7EMkRIJO2d8YEXiySd1NHGw3bv1pBzOOMOVTSQiIop7OuK4cdpn6667kvOcOKaithGLk2jchcloRETkKRj4PmOGliNedFHWF885R/sRo8cW0rmQfbt1q36Ya/RwkAKGRYwaNfSjZs3A7dz/Ri82SjoGtpKNExEpWZC1ddJJIoMHi9x7r8hhhwW+h6DW/v0ideuKNGni5lYSERHFHtjq3VtkyhTteYUxiIn0ySciDz2kt19/nQtDREQe7rP11lu5Gsgjy3b06ED2LQJaCHCZjzVrQv8bxxcMCUHv4vz6Fwc/T7igV/C/ccxiBZdjGNhKNga2KFnQZwsn3WPHijzxhAa4QpUhonSRiIjIb9CrFKUlGIaCi5Wrrkrcc/36q0jPnnobpSo33JC45yIiIkcayP/5p8akkHCVA65/UIZoShHzc+CAyLp1BQfB8IG+Xdu364cZGBcOGkBWq5Z/9pe5XaZMfL+QNMDAVjIhMsxSREp21hYCW6gT79dPpHFj/Tr7axERUapkbSGwhT5biQpsLV4scv75WraCaYzov0JERJ6Fdtbly4ts2yYyZ06gvVZMMGSrTh39KOhaf8eOvIGvUEEw07fLfK0gpUsXXAJZs6ZI1aohonjpIcOysodgRmTjxo3SunVrmThxojTAJBhBa4NR0qdPH1m+fLk0b95chg8fLv8XRUbS9u3bpXz58rJt2zYph9S9VIUXLV5wiM7u2qXjeogSrUsXXcm+/HLtC4IVB+z8YP163QESERH5EZqoHHOMnvSjXMTpcyuUqpxwgmbcY6IWSvnxXERE5GlYv0cbRpQkei7JFmlkCG5FEgRD3CBSGRkizz+vmcUpIJo4UZFog1pdunSRpRiPmWXRokVy9dVXy1tvvSUdOnSQ2267TXr16iVT0O+AQpchYgoDg1qULI8+qoGtYcNE7r9fLwIAJ+gMahERkZ/hWIZV9JUrRcaPF+nc2bnHRvnJxRfr+Vvt2iLffMOgFhGRjxrII7CFyYieC2whqwoJL/jAcSw/O3cW3Ads7VpNXkAWGKYGp6GoAlvdunWTHj16yG9BXdj++ecfefrpp+WSSy6x/33TTTdJZydPKlIJyxDJDVjJRgnFV19pkMsEVVmGSEREfofVaZQjvvGGliM6dQ6KgoZbbtGrIgSzENRCcIuIiHzVZytHA3k/Qn+tRo30Iz+HDmnmcppOZSwUzZ0HDRokvTF9JggyuK6//vrsf8+fP18amz4+Yezbt89OKwv+SAtsHE9uQUALPv1UZORIvc3AFhERpQIEtgCBLaxWO+HFF3Hiq4Gz4cMLXlEnIiJPBrbmzdNe7imvcGGR6tVFypaVdBRVYKshSujysX//fnnhhRfkxhtvzPd+AwYMsGslzUfdunUlLTCwRW5p2VLLKQB7dmRtYWoiERGR3518sp7IoxRj+vT4Hw8Zzn37BgJc55wT/2MSEVFSIcaD4blIwHXi0EApFNgqSP/+/aV06dJ2j6389OvXz24AZj5WrFghaYGliOSmRx7RlWdo35593oiIKDUULy7SqZPeHjUqvsfCbPjLLtMroZtuErn9dkc2kYiIki9lyhEpeYGtCRMmyOuvvy4ff/yxFMVIzHwUL17c7mof/JHyMPoTjU2BGVvkhqZNRa64Qm+j5xYREVGqOO+8QDlirHCehuys3btFzjhD5JVXAgtCRETk28AWGshTanMksLVkyRLp3r27HdhqiotnCp+thZzIihXd3hpKV+gXMmGCB0eDEBERxeGss7S/yJw5IosXR//zmDrVpYtOmGrWTGTECJ1aRUREvp6MaDK2kIhLqSvuwNaePXvsBvLnnXeedO3aVXbu3Gl/WHzlhA5sMVuL3FSsmMgpp4gUcrQKmYiIyF2VKmmZfSxZW5gk1b27yMyZItWqiXz7rUj58gnZTCIiSp5WrXTNA2sWpniKUlPcV7djx46VefPm2RMTy5Ytm/2xbNkyZ7Yw1RrHs78WERERUeLKEaPts3X33RrMQu9J/GyDBgnZPCIiSq5SpXSGFrDPVmqLKbCFbKwGWQd9ZGrh37k/zPcpCyciEhERESXOuefq58mTRTZvjuxn3nhD5OWX9fbQoSJt2iRu+4iIKOnYQD49sB4pWViKSERERJQ4DRuKtGihpYXffVfw/ceMEendW28/+aTIJZckfBOJiCi52EA+PTCwlQwHDogsXKi3WYpIRERElNisrYLKEWfP1kAWgmA9e4r065eUzSMiIncayE+fLnLwoNtbQ4nCwFYyIKiFd1GZMiJ16ri9NURERESp3WcL2Vj79oW+z9q1OgFxxw6RDh1E3n5bJCMjqZtJRETJgbyScuVEdu8WmTvX7a2hRGFgK9mN43niRERERJQYxxwjUrOmyM6dIhMn5v0+rmwQ/Fq+XKRxY5Evv9SJwURElJIwDL51a73NPlupi4GtZPbXYhkiERERUWKvYEw54tdf5/xeZqbIVVdpo5VKlURGj9bPRESU0thAPvUxsJUMnIhIRERElBzBgS3LCnz9wQdFPv9cpGhRkZEjNWOLiIhSHgNbqY+BrWRgYIuIiIgoOU49VaR0aZFVq0RmzNCvvf++yIABevvdd0Xat3d1E4mIKPkN5OfN0/aKlHoY2Eo0rBSyFJGIiIgoOUqUEDnzzMB0RPTauv76QNbWlVe6unlERJRcNWqI1Kunl+aYjkiph4GtRFu5UmTXLpEiRUQaNXJ7a4iIiIjSZzrihx+KXHihTqe+9FKRRx91e8uIiMgFLEdMbUXc3oC0KUNEUAs9HYiIiIgosTp31kbyS5fqv9u00XJEfI2IiNIysPXZZyJPPaWHA1yaJ/sDQ3hj+bmMDLd/e97HwFaisQyRiIiIKLkqVxZp107k559FGjQQ+eorkZIl3d4qIiJySadOIvfcoz22/NZnq3DhyINgd98tcsklknYY2Eo0No4nIiIiSj4sy7/yipYfVq/u9tYQEZGLmjUTWbZMOwUdOJCYj/3743+MQ4fybju+ho+9ewv+/9ywQdISA1uJxsAWERERUfKdeKJ+EBERiUidOvrhZZmZ2hYy1sBYs2aSlhjYSjSWIhIRERERERFRAdAKEr248EGRYwfNRNqyRWTdOr3NwBYRERERERERkaMY2EpGGSLyHcuWdXtriIiIiIiIiIhSCgNbyShDZH8tIiIiIiIiIiLHMbCVjIwtliESERERERERETmOga1E4kREIiIiIiIiIqKEYWArkViKSERERERERESUMAxsJcrevSJLluhtliISERERERERETmOga1EWbBAJDNTpEIFkerV3d4aIiIiIiIiIqKUw8BWMsoQMzLc3hoiIiIiIiIiopTDwFaicCIiEREREREREVFCMbCVKJyISERERERERESUUAxsJQoDW0RERERERERECcXAViIcOqTN44GliERERERERERECcHAViIsWyayd69I8eIiDRu6vTVERERERERERCmJga1EliE2aSJSuLDbW0NERERERERElJIY2EqEf//VzyxDJCIiIiIiIiJKGAa2EqF0aZEWLUSOOsrtLSEiIiIiIiIiSlkZlmVZbm/E9u3bpXz58rJt2zYpV66c25tDREREREREREQ+iBMxY4uIiIiIiIiIiHyJgS0iIiIiIiIiIvIlBraIiIiIiIiIiMiXGNgiIiIiIiIiIiJfYmCLiIiIiIiIiIh8iYEtIiIiIiIiIiLyJQa2iIiIiIiIiIjIlxjYIiIiIiIiIiIiX2Jgi4iIiIiIiIiIfImBLSIiIiIiIiIi8iUGtoiIiIiIiIiIyJcY2CIiIiIiIiIiIl9iYIuIiIiIiIiIiHyJgS0iIiIiIiIiIvKlIuIBlmXZn7dv3+72phARERERERERkYtMfMjEizwf2NqxY4f9uW7dum5vChEREREREREReSReVL58+Xzvk2FFEv5KsMzMTFm9erWULVtWMjIyJFWiiwjUrVixQsqVK+f25pCH8bVCkeJrhSLF1wpFiq8VihRfKxQpvlYoUnytUH4QqkJQq1atWlKoUCHvZ2xhI+vUqSOpCG9QvkkpEnytUKT4WqFI8bVCkeJrhSLF1wpFiq8VihRfKxROQZlaBpvHExERERERERGRLzGwRUREREREREREvsTAVoIUL15c+vfvb38myg9fKxQpvlYoUnytUKT4WqFI8bVCkeJrhSLF1wo5xRPN44mIiIiIiIiIiKLFjC0iIiIiIiIiIvIlBraIiIiIiIiIiMiXGNgiIiIiIiIiIiJfYmCLiIiIiIiIiIh8iYGtGM2ZM0dat24tFStWlL59+0okPfg///xzqV+/vtSqVUuGDx+elO0k940aNUoOO+wwKVKkiBx11FHyzz//FPgz5557rmRkZGR/dOzYMSnbSu7q3bt3jr97o0aNCvyZSZMmyf/93/9JlSpV5MUXX0zKdpK7hgwZkuN1Yj7w9fy0bNkyx/179eqVtG2m5Nq4caM0bNhQli5dGtd5C/DcJf1eK7GctwDPXdLvtRLLeQvw3CW9XiuxnrcAz10oYpiKSNHZu3ev1aBBA+uGG26wFi5caJ199tnW4MGD8/2Z2bNnW8WKFbMGDRpkzZo1y2rUqJH177//Jm2byR14fVSsWNH69NNPrbVr11oXX3yxdcIJJxT4czVr1rRfM1u2bLE/du7cmZTtJXe1bdvWGj16dPbfffv27fnef/369Va5cuWsRx991FqwYIHVqlUra8KECUnbXnLHvn37sl8j+FixYoVVpUoVe38Tzq5du6xSpUr9f3t3EtrkE8Zx/Pnrv9W04tJSFVSqB1EU8SDYivtGPYjrQaXgQXC7KB6EWsXloAdFagWr4kFEEPFiRQT3U8UFpC64gVDQqoUiFVuittiRZyAhjUmavG3emMz3AxHe+A4MZnzeXybvO2PHTKhdMBj0td/wR2trqykrK9NZK9PU1OQ5tyiyi3tjxWtuUWQXt8aKl9yiyC7ujRUvuUWRXZAKJrY8uHr1qr3o63829ezZMzN79uyEbXbu3GkqKirCxydOnDB79+5Ne1+RWdevXzdnz54NH+uFOxAIJGzT3NxsRo8e7UPv8C/p6uqyQa+9vT3pNjU1NWby5Mmmu7vbHtfX15vKyso09hL/osOHD5vNmzcnPKehocGUl5f71idkzuLFi01tbW2PLxVecosiu7g3VrzkFkV2cW+seMktiuzi3ljxklsU2QWp4FFED54/fy7l5eVSUFAQvkXy9evXvbZZtGhR+HjmzJny9OnTtPcVmbV8+XLZsmVL+Pjdu3cyceLEhG2ePHkiv3//lrFjx0phYaGsX79e2trafOgtMunly5fS3d1tH/sIBAKybNky+fDhQ691ZeHChfbWbEVdcc/Pnz+ltrZWqqure60rzc3NUlJSIsOHD5ft27fLr1+/fOsn/HPu3Dn7eFBfc0uoHdnFrbHiJbcosot7Y8VLblFkF/fGipfcosguSAUTWx58//7dPjccooV54MCBCS/g0W2GDh0qnz9/Tntf8e/o7OyU48ePy7Zt2xKe9/btW5k+fbrcuHFDHj16JE1NTbJnzx7f+onM0C+ZkyZNkosXL8qLFy/s2iaRXy5ioa7g0qVLUlZWJuPHj094nn45nTNnjjQ0NMitW7fkzp07UlNT41s/4Z/ImtCX3BKrHTUm98eKl9yiyC7ujRUvuUVRV9yuK8nmFkV2QSr+T+lsWFq4Bw0a1OO9wYMHSzAYtIuyJtMmdD7cceDAAfsrZm+LHmoQjAyDx44dkzVr1siZM2d86CUypbKy0r5C6urqbDjQAKihLxbqCrQuHDx4MKnzIu3fv19OnjwpVVVVaewdsjm3xGpHjXFLsrlFkV3c4yW3KOqK25LNLaFzI5FdkAh3bHlQVFQkra2tPd5rb2+X/Pz8pNv0dj5yy/379+XUqVP2V4q8vLyU2o4cOVK+fv3KrbeO0c9db/H/8uVL3HOoK257//69fS1dutTT+Pr06VNa+oXcyC2x2lFj3NGX3KLILu5JJrco6oq7+pJbFNkFiTCx5YFul/3w4cPwsd5urRduLdTJtmlsbJQxY8akva/IPB0fGzZssAFxypQpvZ6/bt06e8ttiI6bUaNG/fVrO3LL7t277ReIyM99wIABMm7cuLhtqCtuu3Llil0PJ5kvnbNmzZKPHz+Gj3XclJaWprmHyObcEqsdNcYNqeYWRXZxj5fcoqgr7koltyiyC1LBxJYH8+bNs7fZnj9/3h4fOXJElixZYter+Pbtm108M9ratWvl8uXLdqHFjo4OextlRUVFBnoPP/348cMW8JUrV8rq1avtZ68v3ZFUx1BXV9dfbaZNmya7du2yAbG+vt7e2q+LJSK36dok+/btk3v37snt27ftmiYbN260iz3HGysrVqyQBw8eyN27d+3fHz16lLrikJs3b8qCBQt6vBfvGjR16lTZunWrPH78WC5cuGDXzaGuuCNRblFkFySTWxTZBcnkFkV2QTK5RZFd0C9S2kMRYdeuXTMFBQWmuLjYlJSUmFevXtn39Z+0sbExZpvq6mqTn59vt8adMWOGCQaDPvcaftMtjHVMRL90+9vS0lK7BXu0zs5Os2nTJlNYWGi3zj506JDdUhm5r6qqygwbNswUFRWZHTt2mI6ODvt+vLGiTp8+bfLy8syIESPMhAkTTEtLi8+9Ribo9UOvJ2/evOnxfrxrUFtbm1m1apUJBAJ2PNXV1fnYW2RC9Fbr8XJL6Fyyi7six0qi3KLILm6Lrivxcosiu7gteqzEyy2hc8ku6Kv/9I/+mSJzT0tLi92eVrfQLi4uTqqN7iCizwbPnz+f58kB9NtjI7oj1dy5c2XIkCGZ7g6AHMotiuwCoL+RXQD0Jya2AAAAAAAAkJVYYwsAAAAAAABZiYktAAAAAAAAZCUmtgAAAAAAAJCVmNgCAAAAAABAVmJiCwAAAAAAAFmJiS0AAAAAAABkJSa2AAAAAAAAkJWY2AIAAAAAAEBWYmILAAAAAAAAko3+AFzzT4DxxeGKAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "MSE = metrics.mean_squared_error(y_test, y_pred)\n",
    "RMSE = np.sqrt(metrics.mean_squared_error(y_test, y_pred))\n",
    "print('MSE:', MSE)\n",
    "print('RMSE:', RMSE)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.family'] = ['sans-serif']\n",
    "mpl.rcParams['font.sans-serif'] = ['SimHei']\n",
    "mpl.rcParams['axes.unicode_minus']=False\n",
    "# 绘制图\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(range(len(y_test)), y_test, 'r', label='测试数据')\n",
    "plt.plot(range(len(y_test)), y_pred, 'b', label='预测数据')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0802831c-981f-4687-a544-8d3801c3386a",
   "metadata": {},
   "source": [
    "## 作业"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1733640b-6ed2-4ce6-b1b2-95f9a691ac0c",
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "Missing optional dependency 'openpyxl'.  Use pip or conda to install openpyxl.",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
      "\u001b[36mFile \u001b[39m\u001b[32m~\\miniconda3\\Lib\\site-packages\\pandas\\compat\\_optional.py:135\u001b[39m, in \u001b[36mimport_optional_dependency\u001b[39m\u001b[34m(name, extra, errors, min_version)\u001b[39m\n\u001b[32m    134\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m135\u001b[39m     module = \u001b[43mimportlib\u001b[49m\u001b[43m.\u001b[49m\u001b[43mimport_module\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    136\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~\\miniconda3\\Lib\\importlib\\__init__.py:88\u001b[39m, in \u001b[36mimport_module\u001b[39m\u001b[34m(name, package)\u001b[39m\n\u001b[32m     87\u001b[39m         level += \u001b[32m1\u001b[39m\n\u001b[32m---> \u001b[39m\u001b[32m88\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_bootstrap\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_gcd_import\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m[\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpackage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m<frozen importlib._bootstrap>:1387\u001b[39m, in \u001b[36m_gcd_import\u001b[39m\u001b[34m(name, package, level)\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m<frozen importlib._bootstrap>:1360\u001b[39m, in \u001b[36m_find_and_load\u001b[39m\u001b[34m(name, import_)\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m<frozen importlib._bootstrap>:1324\u001b[39m, in \u001b[36m_find_and_load_unlocked\u001b[39m\u001b[34m(name, import_)\u001b[39m\n",
      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'openpyxl'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[31mImportError\u001b[39m                               Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpd\u001b[39;00m\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnp\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m df = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_excel\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mFolds5x2_pp.xlsx\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m      4\u001b[39m data = df[[\u001b[33m'\u001b[39m\u001b[33mAT\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mV\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mAP\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mRH\u001b[39m\u001b[33m'\u001b[39m]]\n\u001b[32m      5\u001b[39m target = df[[\u001b[33m'\u001b[39m\u001b[33mPE\u001b[39m\u001b[33m'\u001b[39m]]\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~\\miniconda3\\Lib\\site-packages\\pandas\\io\\excel\\_base.py:495\u001b[39m, in \u001b[36mread_excel\u001b[39m\u001b[34m(io, sheet_name, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, parse_dates, date_parser, date_format, thousands, decimal, comment, skipfooter, storage_options, dtype_backend, engine_kwargs)\u001b[39m\n\u001b[32m    493\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(io, ExcelFile):\n\u001b[32m    494\u001b[39m     should_close = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m495\u001b[39m     io = \u001b[43mExcelFile\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    496\u001b[39m \u001b[43m        \u001b[49m\u001b[43mio\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    497\u001b[39m \u001b[43m        \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    498\u001b[39m \u001b[43m        \u001b[49m\u001b[43mengine\u001b[49m\u001b[43m=\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    499\u001b[39m \u001b[43m        \u001b[49m\u001b[43mengine_kwargs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mengine_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    500\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    501\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m engine \u001b[38;5;129;01mand\u001b[39;00m engine != io.engine:\n\u001b[32m    502\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m    503\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mEngine should not be specified when passing \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    504\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33man ExcelFile - ExcelFile already has the engine set\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    505\u001b[39m     )\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~\\miniconda3\\Lib\\site-packages\\pandas\\io\\excel\\_base.py:1567\u001b[39m, in \u001b[36mExcelFile.__init__\u001b[39m\u001b[34m(self, path_or_buffer, engine, storage_options, engine_kwargs)\u001b[39m\n\u001b[32m   1564\u001b[39m \u001b[38;5;28mself\u001b[39m.engine = engine\n\u001b[32m   1565\u001b[39m \u001b[38;5;28mself\u001b[39m.storage_options = storage_options\n\u001b[32m-> \u001b[39m\u001b[32m1567\u001b[39m \u001b[38;5;28mself\u001b[39m._reader = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_engines\u001b[49m\u001b[43m[\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   1568\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_io\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1569\u001b[39m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1570\u001b[39m \u001b[43m    \u001b[49m\u001b[43mengine_kwargs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mengine_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1571\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~\\miniconda3\\Lib\\site-packages\\pandas\\io\\excel\\_openpyxl.py:552\u001b[39m, in \u001b[36mOpenpyxlReader.__init__\u001b[39m\u001b[34m(self, filepath_or_buffer, storage_options, engine_kwargs)\u001b[39m\n\u001b[32m    534\u001b[39m \u001b[38;5;129m@doc\u001b[39m(storage_options=_shared_docs[\u001b[33m\"\u001b[39m\u001b[33mstorage_options\u001b[39m\u001b[33m\"\u001b[39m])\n\u001b[32m    535\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\n\u001b[32m    536\u001b[39m     \u001b[38;5;28mself\u001b[39m,\n\u001b[32m   (...)\u001b[39m\u001b[32m    539\u001b[39m     engine_kwargs: \u001b[38;5;28mdict\u001b[39m | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m    540\u001b[39m ) -> \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    541\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m    542\u001b[39m \u001b[33;03m    Reader using openpyxl engine.\u001b[39;00m\n\u001b[32m    543\u001b[39m \n\u001b[32m   (...)\u001b[39m\u001b[32m    550\u001b[39m \u001b[33;03m        Arbitrary keyword arguments passed to excel engine.\u001b[39;00m\n\u001b[32m    551\u001b[39m \u001b[33;03m    \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m552\u001b[39m     \u001b[43mimport_optional_dependency\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mopenpyxl\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m    553\u001b[39m     \u001b[38;5;28msuper\u001b[39m().\u001b[34m__init__\u001b[39m(\n\u001b[32m    554\u001b[39m         filepath_or_buffer,\n\u001b[32m    555\u001b[39m         storage_options=storage_options,\n\u001b[32m    556\u001b[39m         engine_kwargs=engine_kwargs,\n\u001b[32m    557\u001b[39m     )\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~\\miniconda3\\Lib\\site-packages\\pandas\\compat\\_optional.py:138\u001b[39m, in \u001b[36mimport_optional_dependency\u001b[39m\u001b[34m(name, extra, errors, min_version)\u001b[39m\n\u001b[32m    136\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[32m    137\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m errors == \u001b[33m\"\u001b[39m\u001b[33mraise\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m138\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(msg)\n\u001b[32m    139\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m    141\u001b[39m \u001b[38;5;66;03m# Handle submodules: if we have submodule, grab parent module from sys.modules\u001b[39;00m\n",
      "\u001b[31mImportError\u001b[39m: Missing optional dependency 'openpyxl'.  Use pip or conda to install openpyxl."
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.read_excel('Folds5x2_pp.xlsx')\n",
    "data = df[['AT','V','AP','RH']]\n",
    "target = df[['PE']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea7fc4c1-42b4-4066-948e-d3fd11f6da39",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data,target, test_size=0.4, random_state=100)\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "y_pred = lr.predict(X_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "MSE = metrics.mean_squared_error(y_test, y_pred)\n",
    "RMSE = np.sqrt(metrics.mean_squared_error(y_test, y_pred))\n",
    "print('MSE:', MSE)\n",
    "print('RMSE:', RMSE)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.family'] = ['sans-serif']\n",
    "mpl.rcParams['font.sans-serif'] = ['SimHei']\n",
    "mpl.rcParams['axes.unicode_minus']=False\n",
    "# 绘制图\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(range(len(y_test)), y_test, 'r', label='ceshi')\n",
    "plt.plot(range(len(y_test)), y_pred, 'b', label='yuce')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
